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Contributed Talk/ Poster Submission has been closed on 1 March.
Programme
Presentation Instructions
Conference Days
-
Satellite Workshop Day 1
16 June 2025 -
Satellite Workshop Day 2
17 June 2025 -
Conference Day 1
18 June 2025 -
Conference Day 2
19 June 2025 -
Conference Day 3
20 June 2025
May subject to changes without prior notice
Download Abstracts of the Day-
Satellite Workshop Day 1 - 16 June 202508:00am - 09:00am Satellite Workshop 1:
Bayesian Computation and Inference with Misspecified Models Auditorium 2 Registration & Networking08:00am - 11:15am Satellite Workshop 2:
Bayesian Methods for Distributional and Semiparametric Regression LT50 Registration & Networking09:00am – 09:45am Satellite Workshop 1:
Bayesian Computation and Inference with Misspecified Models Auditorium 2 Robust and Conjugate Gaussian Process Regression Matias Altamirano, University College London,
UNITED KINGDOM09:45am – 10:30am Satellite Workshop 1:
Bayesian Computation and Inference with Misspecified Models Auditorium 2 Misspecification in Gaussian Process Regression Assc Prof. Aretha Teckentrup, University of Edinburgh,
UNITED KINGDOM10:30am – 11:00am Satellite Workshop 1:
Bayesian Computation and Inference with Misspecified Models Auditorium 2 Break11:00am – 11:45am Satellite Workshop 1:
Bayesian Computation and Inference with Misspecified Models Auditorium 2
Generalisation Analysis for Active Learning Under Model Misspecification Roubing Tang, University of Manchester,
UNITED KINGDOM
Multi-scale Uncertainties in Fracture Conductivity for CO2 Storage under Model Misspecification Dr. Sarah Perez, Heriot-Watt University,
UNITED KINGDOM11:15am – 11:45am Satellite Workshop 2:
Bayesian Methods for Distributional and Semiparametric Regression LT50 Welcome & Opening Address Prof. Nadja Klein, Karlsruhe Institute of Technology,
GERMANY Dr. Lucas Kock, National University of Singapore,
SINGAPORE11:45am – 12:30pm Satellite Workshop 1:
Bayesian Computation and Inference with Misspecified Models Auditorium 2 Robust Bayesian Inference with Possibility Theory Asst Prof. Jeremie Houssineau, Nanyang Technological University,
SINGAPORESatellite Workshop 2:
Bayesian Methods for Distributional and Semiparametric Regression LT50 Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices Prof. Michael Stanley Smith, University of Melbourne,
AUSTRALIA12:30pm – 02:00pm Lunch02:00pm – 03:30pm Satellite Workshop 1:
Bayesian Computation and Inference with Misspecified Models Auditorium 2
A Principled Approach to Bayesian Transfer Learning Adam Bretheron, Queesnland University of Technology,
AUSTRALIA
Predictive Performance of Power Posteriors Yann McLatchie, University College London,
UNITED KINGDOM
A Unifying Framework for Generalised Bayesian Online Learning in Non-stationary Environments Gerardo Duran-Martin, Queen Mary University,
UNITED KINGDOM02:00pm – 02:45pm Satellite Workshop 2:
Bayesian Methods for Distributional and Semiparametric Regression LT50 Varying-coefficients Bayesian models for Inference of Networks and Covariate Effects Prof. Marina Vannucci, Rice University,
UNITED STATES OF AMERICA02:45pm – 03:30pm Satellite Workshop 2:
Bayesian Methods for Distributional and Semiparametric Regression LT50 Towards Flexibility and Efficiency of Gaussian Process State-Space Models Dr. Zhidi Lin, National University of Singapore,
SINGAPORE03:30pm – 04:00pm Break04:00pm – 05:00pm Satellite Workshop 1:
Bayesian Computation and Inference with Misspecified Models Auditorium 2 Likelihood Distortion and Bayesian Local Robustness Prof. Antonietta Mira, Università Della Svizzera Italiana
ITALYSatellite Workshop 2:
Bayesian Methods for Distributional and Semiparametric Regression LT50 ProDAG: Projection-Induced Variational Inference for Directed Acyclic Graphs Prof. Robert Kohn, University of New South Wales,
AUSTRALIA05:00pm – 06:00pm Satellite Workshop 1:
Bayesian Computation and Inference with Misspecified Models Auditorium 2 Model Misspecification in Martingale Posteriors Asst Prof. Edwin Fong, University of Hong Kong,
HONG KONG SAR, CHINA04:45pm – 06:00pm Satellite Workshop 2:
Bayesian Methods for Distributional and Semiparametric Regression LT50 Copula-based Models for Spatially Dependent Cylindrical Data Francesca Labanca, University of Florence,
ITALY -
Satellite Workshop Day 2 - 17 June 202508:00am – 09:00am Registration & Networking09:00am – 10:30am Satellite Workshop 1:
Bayesian Computation and Inference with Misspecified Models Auditorium 2 Post-Bayesian Inference Assc. Prof. Jeremias Knoblauch, University College London,
UNITED KINGDOM09:00am – 09:45am Satellite Workshop 2:
Bayesian Methods for Distributional and Semiparametric Regression LT50 Radial Neighbours for Provably Accurate Scalable Approximations of Gaussian Processes Assc Prof. Cheng Li, National University of Singapore,
SINGAPORE09:45am - 10:30am Satellite Workshop 2:
Bayesian Methods for Distributional and Semiparametric Regression LT50 Bayesian Structural Learning: Applications in Biological Systems Prof. Maria De Lorio, National University of Singapore,
SINGAPORE10:30am – 11:00am Break11:00am – 11:45am Satellite Workshop 1:
Bayesian Computation and Inference with Misspecified Models Auditorium 2 Resampling within MCMC: Approaches, Computational Benefits, and Statistical Properties Asst. Prof. Jonathan Huggins, Boston University,
UNITED STATES OF AMERICASatellite Workshop 2:
Bayesian Methods for Distributional and Semiparametric Regression LT50 Time-varying Multi-seasonal ARMA Processes with Semiparametric Evolution Over Time Prof. Mattias Villani, Stockholm University,
SWEDEN11:45am - 12:30pm Satellite Workshop 1:
Bayesian Computation and Inference with Misspecified Models Auditorium 2 Model-based Distributionally Robust Optimisation: Bayesian Ambiguity Sets and Model Misspecification Dr. Harita Dellaporta, University College London,
UNITED KINGDOMSatellite Workshop 2:
Bayesian Methods for Distributional and Semiparametric Regression LT50 Monte Carlo Inference for Semiparametric Bayesian Regression Dr. Bohan Wu, Columbia University,
UNITED STATES OF AMERICA12:30pm – 02:00pm Lunch02:00pm – 03:30pm Satellite Workshop 1:
Bayesian Computation and Inference with Misspecified Models Auditorium 2
Robust Simulation-based Inference under Missing Data Dr. Ayush Bharti, Aalto University,
FINLAND
Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference Ryan Kelly, Queensland University of Technology,
AUSTRALIA
Prediction-centric Uncertainty Quantification via MMD Prof. Chris Oates, Newcastle University,
UNITED KINGDOM02:00pm - 02:45pm Satellite Workshop 2:
Bayesian Methods for Distributional and Semiparametric Regression LT50 Semi-Parametric Local Variable Selection under Misspecification Prof. David Rossell, Pompeu Fabra University,
SPAIN02:45pm - 03:30pm Satellite Workshop 2:
Bayesian Methods for Distributional and Semiparametric Regression LT50 Bayesian Function-on-Function Regression for Spatial Functional Data Asst. Prof. Jaewoo Park, Yonsei University,
SOUTH KOREA03:30pm – 04:00pm Break04:00pm – 05:00pm Satellite Workshop 1:
Bayesian Computation and Inference with Misspecified Models Auditorium 2 A New Mutual Information Bound for Statistical Inference Prof. Pierre Alquier, ESSEC Business School,
FRANCESatellite Workshop 2:
Bayesian Methods for Distributional and Semiparametric Regression LT50 Regression with Random Rectangle Summaries and Variational Transdimensional Inference Prof. Scott A. Sisson, University of New South Wales,
AUSTRALIA05:00pm - 06:00pm Satellite Workshop 1:
Bayesian Computation and Inference with Misspecified Models Auditorium 2 Data-centric Semi-modular Bayesian Inference Prof. David T. Frazier, Monash University,
AUSTRALIASatellite Workshop 2:
Bayesian Methods for Distributional and Semiparametric Regression LT50 Orthogonal Calibration via Posterior Projections with Applications to the Schwarzschild Model Asst. Prof. Antik Chakraborty, Purdue University,
UNITED STATES OF AMERICA -
Conference Day 1 - 18 June 202508:00am – 09:00am Auditorium 2 Registration & Networking09:00am - 09:10am Auditorium 2 Welcome & Opening Address Prof. David T. Frazier, Monash University,
AUSTRALIA Assc Prof. David Nott, National University of Singapore,
SINGAPORE09:10am – 10:00am Keynote 1 Auditorium 2 Some progress on unbiased MCMC Prof. Pierre E. Jacob, ESSEC Business School,
FRANCE Chair:
Prof. Chris Oates, Newcastle University,
UNITED KINGDOM10:10am – 10:30am Auditorium 2 BreakParallel Invited Sessions
10:30am – 12:00pmAdvances in Efficient Bayesian Inference for Complex Multivariate Models Auditorium 2Organiser and Chair:
Dr. Linda Tan Siew Li, National University of Singapore,
SINGAPORE
Bayesian Regularized Regression Copula Processes for Multivariate Responses Prof. Nadja Klein, Karlsruhe Institute of Technology,
GERMANY
Variational Approximate Inference in Structural Equation Modeling Dr Luca Maestrini, Australian National University,
AUSTRALIA
A Generalized Functional Delta Method with Applications to Bayesian Inference Assc Prof. Hien Nguyen, La Trobe University,
AUSTRALIA(Almost) Gradient-based Markov chain Monte Carlo Algorithms LT50Organiser and Chair:
Dr. Dootika Vats, Indian Institute of Technology Kanpur,
INDIA
Proximal Interacting Particle Langevin Algorithms Asst Prof. Francesca Romana Crucinio, King’s College London,
UNITED KINGDOM
Stereographic Multi-Try Metropolis Algorithms for Heavy-tailed Sampling Zhihao Wang, University of Copenhagen,
DENMARK
Repelling-Attracting Hamiltonian Monte Carlo for High Dimensional Multimodality Asst Prof. Siddharth Vishwanath, University of California,
UNITED STATES OF AMERICAScalable Causal Inference LT51Organiser and Chair:
Prof. Maria De Iorio, National University of Singapore,
SINGAPORE
Inference for Enriched Dirichlet Process Mixtures for Large n and a Rare Outcome with Missingness Prof. Michael J. Daniels, University of Florida,
UNITED STATES OF AMERICA
Fast Machine Learning Causal Network Analysis Using Genetic Instruments Hui Guo, University of Manchester,
UNITED KINGDOM
Efficient Sampling for Bayesian Networks and Benchmarking Their Structure Learning Dr. Jack Kuipers, ETH Zürich,
SWITZERLANDComparison Theory for Modern MCMC Methods Global Learning RoomOrganiser and Chair:
Dr. Andi Q. Wang, University of Warwick,
UNITED KINGDOM
Analysis of Multiple-try Metropolis via Poincaré Inequalities Rocco Caprio, University of Warwick,
UNITED KINGDOM
Spectral Gap Bounds for Reversible Hybrid Gibbs Chains Dr. Guangyang Wang, University of Minnesota,
UNITED STATES OF AMERICA
Gradient-free Parallel Sampling Prof. Giacomo Zanella, Bocconi University,
ITALY12:00pm – 01:45pm LunchParallel Invited Sessions
01:45pm – 03:00pmAdvances in Efficient Bayesian Inference for Complex Multivariate Models Auditorium 2Organiser and Chair:
Prof. Maria De Iorio, National University of Singapore,
SINGAPORE
Graph of Graphs: From Nodes to Supernodes in Graphical Models Prof. Alexandros Beskos, University College London,
UNITED KINGDOM
ABC for Network Models via a Multi-scale Summary Statistic Prof. Antonietta Mira, Università della Svizzera italiana,
SWITZERLAND
Bayesian Structural Learning with Parametric Marginals for Count Data: An Application to Microbiota Systems Dr. Pariya Behrouzi, Wageningen University and Research,
THE NETHERLANDSBayesian Computation in Astrophysics LT50Organiser and Chair:
Dr. Kate Lee, University of Auckland,
NEW ZEALAND
Accelerating Bayesian Inference on Populations of Merging Binary Black Hole via Active Learning Dr. Avi Vajpeyi, University of Auckland,
NEW ZEALAND
Novel Sampling Algorithms for Posterior Estimation in the Physical Sciences Asst Prof Alvin Chua, National University of Singapore,
SINGAPORE
Bayesian Parameter Estimation and Model Selection Problems in Astrophysics Asst Prof Joshua S. Speagle, University of Toronto,
CANADAManifold Markov Chain Monte Carlo and Beyond LT51Organiser and Chair:
Prof. Daniel Rudolf, University of Passau,
GERMANY
Geodesic Slice Sampling on Riemannian Manifolds Dr. Mareike Hasenpflug, University of Passau,
GERMANY
Adaptive Stereographic MCMC Cameron Bell, University of Warwick,
UNITED KINGDOM
Dimension-independent Markov Chain Monte Carlo on the Sphere Dr. Björn Sprungk, TU Bergakademie Freiberg,
GERMANYModel Misspecification in Simulation-based Inference Global Learning RoomOrganiser and Chair:
Dr. Ayush Bharti, Aalto University,
FINLAND
Multi-level Neural Simulation-based Inference Assc Prof. Francois-Xavier Briol, University College London,
UNITED KINGDOM
Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data Prof. Paul-Christian Bürkner, TU Dortmund University,
GERMANY
Addressing Misspecification in Simulation-based Inference through Data-driven Calibration Dr. Antoine Wehenkel, Apple Inc.,
SWITZERLAND03:10pm – 03:30pm BreakParallel Contributed Sessions
03:30pm – 05:30pmApproximate Inference Methods Auditorium 2Chair:
Prof. David T. Frazier, Monash University,
AUSTRALIA
An Adaptive Approximate Bayesian Computation MCMC with Global-Local Proposals Asst Prof. Shijia Wang, ShanghaiTech University,
CHINA
Preconditioned Neural Posterior Estimation for Likelihood-free Inference Prof. Chirstopher Drovandi, Queensland University of Technology,
AUSTRALIA
Efficient Tuning of Multifidelity Simulation-based Inference for Computationally Expensive Simulators Dr David J. Warne, Queensland University of Technology,
AUSTRALIA
Model-based Distributionally Robust Optimisation: Bayesian Ambiguity Sets and Model Misspecification Dr Harita Dellaborta, University of Warwick,
UNITED KINGDOM
Divide-and-conquer SMC for Integrating Bayesian models using Markov Melding Dr Yixuan Liu, University of Cambridge,
UNITED KINGDOM
A Closed-Form Transition Density Expansion for Elliptic and Hypo-Elliptic SDEs Yuga Iguchi, University College London,
UNITED KINGDOMModular Inference, Predictive Inference and Sparsity LT50Chair:
Asst. Prof. Yan Shuo Tan, National University of Singapore,
SINGAPORE
New (and Old) Predictive Schemes with “a.c.i.d.” Sequences Asst Prof. Lorenzo Cappello, Universitat Pompeu Fabra,
SPAIN
Validating uncertainty propagation approaches for two-stage Bayesian spatial models using simulation-based calibration Stephen Jun Villejo, University of Glasgow,
UNITED KINGDOM University of the Philippines,
PHILIPPINES
A General Framework for Cutting Feedback in Bayesian Models Dr. Robert Goudie, University of Cambridge,
UNITED KINGDOM
Bayesian Nonparametric Hypothesis Testing Methods on Multiple Comparisons Asst Prof. Zhuanzhuan Ma, The University of Texas Rio Grande Valley,
UNITED STATES OF AMERICA
A Bayesian Regression for Directional Responses Assc Prof. Subhadip Pal, UAE University,
UNITED ARAB EMIRATES
Group Penalised Credible Region for Bayesian Variable Selection Khue-Dung Dang, The University of Western Australia,
AUSTRALIAComplex Modelling and Computation LT51Chair:
Dr Jack Jewson, Monash University,
AUSTRALIA
Exact Sampling of Gibbs Measures with Estimated Losses Dr Jack Jewson, Monash University,
AUSTRALIA
Gaussian Process Surrogates for Bayesian Inverse Problems Asst Prof Jonathan Huggins, Boston University,
UNITED STATES OF AMERICA
Saddlepoint Monte Carlo and its Application to Exact Ecological Inference Dr Robin Ryder, Imperial College London,
UNITED KINGDOM
Stochastic Volatility with Informative Missingness Asst Prof Gehui Zhang, Southwest Petroleum University,
CHINA University of Pittsburgh,
UNITED STATES OF AMERICA
Fast Marginal Likelihood Inference for Fitting Stochastic Epidemic Models Assc. Prof. Jason Xu, UCLA,
UNITED STATES OF AMERICA
SBAMDT: Bayesian Additive Decision Trees with Adaptive Soft Semi-multivariate Split Rules Huiyan Sang, Texas A&M University,
UNITED STATES OF AMERICAAdvanced Monte Carlo Methods Global Learning RoomChair:
Assc. Prof. Alexandre Thiery, National University of Singapore,
SINGAPORE
An Invitation to Adaptive MCMC Convergence Theory Prof Matti Vihola, University of Jyväskylä,
FINLAND
Sequential Exchange Monte Carlo: Sampling Method for Multimodal Distribution without Parameter Tuning Tomohiro Nabika, University of Tokyo,
JAPAN
Bayesian Posterior Sampling with Adaptive SMC for Efficient Exploration in Reinforcement Learning Jiaqi Guo, University of Cambridge,
UNITED KINGDOM
Sequential Gaussian Processes for Online Learning of Nonstationary Functions Asst Prof Michael Zhang, The University of Hong Kong,
HONG KONG SAR, CHINA
Guided Particle Filters for Continuous-time Processes Prof Frank van der Meulen, Vrije Universiteit Amsterdam,
THE NETHERLANDS
Sampling with Time-changed Markov Processes Dr Giorgos Vasdekis, Newcastle University,
UNITED KINGDOM05:30pm – 07:30pm Auditorium 2 & LT50 Poster Session & Light Dinner -
Conference Day 2 - 19 June 202508:00am – 09:00am Auditorium 2 Registration & Networking09:00am – 10:00am Keynote 2 Auditorium 2 A Dynamic Horseshoe Process Prior and Beyond Prof. Sylvia Frühwirth-Schnatter, WU Vienna University of Economics and Business,
AUSTRIA Chair:
Prof. Christian Robert, Université Paris Dauphine,
FRANCE University of Warwick,
UNITED KINGDOM10:00am – 10:20am Auditorium 2 BreakParallel Invited Sessions
10:20am – 11:50amHigh-dimensional Discrete Model Search Auditorium 2Organiser and Chair:
Prof. David Rossell, Pompeu Fabra University,
SPAIN
Zero-order Parallel Sampling Francesco Pozza, Bocconi University,
ITALY
Convergence Analysis of Markov Chain Monte Carlo Methods for Model Selection Problems Hyunwoong Chang, University of Texas at Dallas,
UNITED STATES OF AMERICA
Bayesian Computation for High-dimensional Gaussian Graphical Models with Spike-and-slab Prior Prof. Déborah Sulem, Universitá de la Svizzera Italiana,
SWITZERLANDAdvances in Variational Inference LT50Organiser and Chair:
Prof. Randal Douc, Telecom SudParis,
FRANCE
Near-Optimal Approximations for Bayesian Inference in Function Space Assc Prof. Jeremias Knoblauch, University College London,
UNITED KINGDOM
Learning Symmetries with Variational Inference Dr. Charles Margossian, Flatiron Institute,
UNITED STATES OF AMERICA
Learning with Importance Weighted Variational Inference Asst Prof. Kamélia Daudel, ESSEC Business School,
FRANCEAdvanced Langevin Methods for Bayesian Sampling LT51Organiser and Chair:
Dr. Neil K. Chada, City University of Hong Kong,
HONG KONG SAR, CHINA
Gradient Flows for Statistical Computation - Trends and Trajectories Dr Sam Power, University of Bristol,
UNITED KINGDOM
Explicit Convergence Rates of Underdamped Langevin Dynamics under weighted and Weak Poincaré–Lions Inequalities Dr Andi Wang, University of Warwick,
UNITED KINGDOM
Randomized Splitting Methods and Stochastic Gradient Algorithms Dr Peter Whalley, ETH Zürich,
SWITZERLANDComputation-enabled Bayesian Inference and Prediction Global Learning RoomOrganiser:
Prof. Li Ma, Duke University,
UNITED STATES OF AMERICA Chair:
Prof. David Frazier, Monash University,
AUSTRALIA
Predictive Resampling for Scalable Bayes Asst Prof. Edwin Fong, University of Hong Kong,
HONG KONG SAR, CHINA
Lessons on Mixing for Bayesian Additive Regression Trees Asst Prof. Yan Shuo Tan, National University of Singapore,
SINGAPORE
Tree Boosting for Learning Density Ratios with Generalized Bayesian uUcertainty Quantification Asst Prof. Naoki Awaya, Waseda University,
JAPAN11:50am – 01:40pm Lunch01:40pm – 03:10pm Panel Discussion Bayesian Inference and Machine Learning: A Two-way Street Auditorium 2 Moderator:
Prof. David T. Frazier, Monash University,
AUSTRALIA Prof. Sylvia Fruhwirth-Schnatter, WU Vienna University of Economics and Business,
AUSTRIA Prof. Pierre E. Jacob, ESSEC Business School,
FRANCE Prof. Mohammad Emtiyaz Khan, RIKEN Center for Advanced Intelligence Project,
JAPAN03:10pm – 03:30pm BreakParallel Invited Sessions
03:30pm – 05:30pmSimulation-based Bayesian Inference: Efficiency, Robustness, and Theoretical Results Auditorium 2Organiser and Chair:
Prof. Christopher Drovandi, Queensland University of Technology,
AUSTRALIA
The Statistical Accuracy of Neural Posterior and Likelihood Estimation Prof. David T. Frazier, Monash University,
AUSTRALIA
Scalable SBI via Optimization-based Acceleration Asst Prof. Yuexi Wang, University of Illinois Urbana-Champaign,
UNITED STATES OF AMERICA
Misspecification-robust Methods for SBI with Neural Conditional Density Estimators Ryan Kelly, Queensland University of Technology,
AUSTRALIARecalibration Methods for Improved Bayesian Inference from Approximate Models LT50Organiser and Chair:
Dr. Joshua Bon, Université Paris Dauphine,
FRANCE
Calibrating/Recalibrating Approximate Bayesian Credible Sets Dr Jeong E (Kate) Lee, University of Auckland,
NEW ZEALAND
Model-Free Local Recalibration of Neural Networks Dr Guilherme Souza Rodrigues, University of Brasilia,
BRAZIL
Variational Transdimensional Inference Prof. Scott A. Sisson, University of New South Wales,
AUSTRALIACutting Feedback: Methods and Applications in Bayesian Settings LT51Organiser:
Dr. Anne Presanis, MRC Biostatistics Unit, University of Cambridge,
UNITED KINGDOM Chair:
Daniela De Angelis, MRC Biostatistics Unit, University of Cambridge,
UNITED KINGDOM
Asymptotics of Cut Posteriors and Robust Modular Inference Asst Prof. Mikolaj Kasprzak, ESSEC Business School,
FRANCE
Cutting Feedback and Modularized Analyses in Generalized Bayesian Inference Assc Prof. David Nott, National University of Singapore,
SINGAPORE
On the Use of Cut-posteriors for Causal Factor Analysis Pantelis Samartsidis, University of Cambridge,
UNITED KINGDOM
Discussant:
Prof Daniela De Angelis, MRC Biostatistics Unit, University of Cambridge,
UNITED KINGDOMPDMPs in Practice Global Learning RoomOrganiser:
Luke Harcastle, University College London,
UNITED KINGDOM Chair:
Dr. Sebastiano Grazzi, Bocconi University,
ITALY
Automated Techniques for Efficient Sampling of Piecewise-Deterministic Markov Processes Prof. Kengo Kamatani, The Institute of Statistical Mathematics,
JAPAN
Sampling Diffusion Piecewise Exponential Models using Piecewise Deterministic Markov Processes Luke Hardcastle, University College London,
UNITED KINGDOM
Zig-zag Sampling for Discrete Variables in Phylogenetics Dr Jere Koskela, University of Newcastle,
UNITED KINGDOM
Discussant:
Dr. Sebastiano Grazzi, Bocconi University,
ITALY05:30pm – 07:30pm Auditorium 2 & LT50 Poster Session & Light Dinner -
Conference Day 3 - 20 June 202508:00am – 09:00am Auditorium 2 Registration & Networking09:00am – 10:00am Keynote 3 Auditorium 2 Adaptive Bayesian Intelligence Prof. Mohammad Emtiyaz Khan, RIKEN Center for Advanced Intelligence Project,
JAPAN Chair:
Prof. David T. Frazier, Monash University,
AUSTRALIA10:00am – 10:20am Auditorium 2 BreakParallel Invited Sessions
10:20am – 11:50amBayesian Federated Learning Auditorium 2Organiser:
Prof. Christian Robert, Université Paris Dauphine,
FRANCE University of Warwick,
UNITED KINGDOM
Confidential Accept-Reject Assc Prof. Louis J.M. Aslett, Durham University,
UNITED KINGDOM
Breaking Dependencies to enable Federated Learning of Bayesian Models Dr Conor Hassan, Aalto University,
FINLAND
Likelihood-free Bayesian Model Selection via Sequential Neural Likelihood Estimation Prof. Jean-Michel Marin, Université de Montpellier,
FRANCEFlexible Approximate Inference Methods LT50Organiser and Chair:
Assc Prof. David Nott, National University of Singapore,
SINGAPORE
Weighted Fisher Divergence for High-dimensional Gaussian Variational Inference Dr Linda Tan, National University of Singapore,
SINGAPORE
High-Dimensional Multivariate Stochastic Volatility Model Based on A Copula of A Mixture Prof. Robert Kohn, University of New South Wales,
AUSTRALIA
Applying Multi-objective Bayesian Optimization to Likelihood-free Inference David Chen National University of Singapore,
SINGAPOREAdvances in Methodology for Sequential Monte Carlo in High Dimensions LT51Organiser and Chair:
Prof. Alexandros Beskos, University College London,
UNITED KINGDOM
Mixing Time of the Conditional Backward Sampling Particle Filter Prof. Sumeetpal Singh, University of Wollongong,
AUSTRALIA
Particle-MALA and Particle-mGRAD: Gradient-based MCMC Methods for High-dimensional State-space Models Assc Prof. Axel Finke, Newcastle University,
UNITED KINGDOM
Sequential Monte Carlo from a Multiple Importance Sampling Perspective Prof. Victor Elvira, University of Edinburgh,
UNITED KINGDOMComputationally Efficient Bayesian Inference Global Learning RoomChair:
Assc. Prof. Li Cheng, National University of Singapore,,
SINGAPORE
Cost-aware Simulation-based Inference Dr. Ayush Bharti, Aalto University,
FINLAND
Probabilistic Richardson Extrapolation Prof. Chris. J. Oates, Newcastle University,
UNITED KINGDOM
Extrapolation and Smoothing of Tempered Posteriors Dr. Marina Riabiz, King's College London,
UNITED KINGDOM11:50am – 01:10pm LunchParallel Invited Sessions
01:10pm – 02:40pmRecent advances in Sequential Monte Carlo methods Auditorium 2Organiser and Chair:
Dr. Hai-Dang Dau, National University of Singapore,
SINGAPORE
On the Stability of Schrödinger Bridges and Sinkhorn Semigroups Prof. Pierre Del Moral, NRIA Bordeaux,
FRANCE
A Random-walk in the Land of Denoising Diffusions Dr. Adrien Corenflos, University of Warwick,
UNITED KINGDOM
Scalable Bayesian Inference for Large Language Model Analysis Asst. Prof Ning Ning, Texas A&M University,
UNITED STATES OF AMERICAComputation and model criticism for highly parametrized Bayesian models LT50Organiser and Chair:
Assc Prof. David Nott, National University of Singapore,
SINGAPORE
Latent Random Partition Models: An Application to Childhood Co-morbidity Prof. Maria De Lorio, National University of Singapore,
SINGAPORE
Truly Multivariate Structured Additive Distributional Regression Dr. Lucas Kock, National University of Singapore,
SINGAPORE
Cutting Feedback in Misspecified Copula Models Prof. Michael Stanley Smith, University of Melbourne,
AUSTRALIAApproximate Methods for Accelerated Sampling LT51Organiser and Chair:
Asst. Prof. Yuexi Wang, University of Illinois Urbana-Champaign,
UNITED STATES OF AMERICA
Learning Summary Statistics for Likelihood-free Bayesian Inference Asst Prof. Rong Tang, Hong Kong University of Science and Technology,
HONG KONG SAR, CHINA
Estimating the Number of Components in Finite Mixture Models via Variational Approximation Assc Prof. Yun Yang, University of Maryland, College Park,
UNITED STATES OF AMERICA
Graph-accelerated Markov Chain Monte Carlo using Approximate Samples Asst. Prof. Leo Duan, University of Florida,
UNITED STATES OF AMERICAOptimization and Control for Sampling Global Learning RoomOrganiser and Chair:
Assc. Prof. Alexandre Thiery, National University of Singapore,
SINGAPORE
A Dynamical Systems Perspective on Measure Transport and Generative Modeling Dr. Lorenz Richter, Zuse Institute Berlin,
GERMANY
Transport meets Variational Inference: Controlled Monte Carlo Diffusions Dr. Nikolas Nüsken, King's College London,
UNITED KINGDOM
Stochastic Control for Black-box Inference: Insights from Deep Reinforcement Learning Dr. Nikolay Malkin, University of Edinburgh,
UNITED KINGDOM02:40pm – 03:00pm BreakParallel Invited Sessions
03:00pm – 04:30pmSurrogate Models and Kernel Methods Auditorium 2Organiser and Chair:
Prof. Chris. J. Oates, Newcastle University,
UNITED KINGDOM
Squared Neural Probabilistic Models Prof. Dino Sejdinovic, University of Adelaide,
AUSTRALIA
Fast and Scalable Sequential Experimental Design via Subspace-Accelerated Transport Map Surrogates Dr. Karina Koval, Heidelberg University,
GERMANY
High-dimensional Kernel Approximation with Length-scale Informed Sparse Grids Assc Prof. Aretha Teckentrup, University of Edinburgh,
UNITED KINGDOMAdvances in Heavy-Tailed Sampling: Bridging Theory and Practice LT50Organiser:
Dr. Alex Shestopaloff, Queen Mary University of London,
UNITED KINGDOM
Rapid Mixing of Stereographic MCMC for Heavy-tailed Sampling Federica Milinanni, KTH Royal Institute of Technology,
SWEDEN
Stereographic Barker's MCMC Proposal: Efficiency and Robustness at Your Disposal Asst. Prof. Jun Yang, University of Copenhagen,
DENMARK
Characterization and Control of Global Dynamics of Heavy-tailed SGD Asst. Prof. Chang-Han Rhee, Northwestern University,
UNITED STATES OF AMERICAVariational Bayes for Uncertainty Quantification LT51Organiser and Chair:
Asst. Prof. Ryan Giordano, University of California, Berkeley,
UNITED STATES OF AMERICA Asst. Prof. Alex Strang, University of California, Berkeley,
UNITED STATES OF AMERICA
Making Variational Inference Work for Statisticians: Parallel Tempering with a Variational Reference Assc Prof. Trevor Campbell, University of British Columbia,
CANADA
Batch and Match: Score-based Approaches for Black-box Variational Inference Dr. Diana Cai, Flatiron Institute,
UNITED STATES OF AMERICA
Predictive Variational Inference: Learn the Predictively Optimal Posterior Distribution Asst Prof. Yuling Yao, University of Texas at Austin,
UNITED STATES OF AMERICAParallel Computations for Markov Chain Monte Carlo Global Learning RoomOrganiser:
Dr. Sebastiano Grazzi, Bocconi University
ITALY Chair:
Dr. Charles Margossian, Flatiron Institute
UNITED STATES OF AMERICA
Output Analysis for Parallel MCMC Dr. Dootika Vats, Indian Institute of Technology Kanpur
INDIA
Parallelized Midpoint Randomization for Langevin Monte Carlo Asst Prof. Lu Yu, City University of Hong Kong
HONG KONG SAR, CHINA
Parallel Computations for Metropolis Markov Chains with Picard Maps Dr. Sebastiano Grazzi, Bocconi University
ITALY04:30pm Auditorium 2 Awards and Closing Remarks Prof. David T. Frazier, Monash University,
AUSTRALIA Assc Prof. David Nott, National University of Singapore,
SINGAPORE
Invited Sessions
(Almost) Gradient-based Markov chain Monte Carlo Algorithms
Speakers
- Dr. Francesca Romana Crucinio, King’s College London
- Dr. Zhihao Wang, University of Copenhagen
- Dr. Siddharth Vishwanath, University of California, San Diego
Chair/Organiser
- Dr. Dootika Vats, Indian Institute of Technology Kanpur
Advanced Langevin Methods for Bayesian Sampling
Speakers
- Dr. Sam Power, University of Bristol
- Dr. Andi Wang, University of Warwick
- Dr. Peter Whalley, ETH Zurich
Chair/Organiser
- Dr. Neil K. Chada, City University of Hong Kong
Advances in efficient Bayesian inference for complex multivariate models
Speakers
- Prof. Nadja Klein, Karlsruhe Institute of Technology, Germany
- Dr. Luca Maestrini, Australian National University, Australia
- Associate Prof. Hien Nguyen, La Trobe University, Australia
Chair/Organiser
- Dr. Linda Tan, National University of Singapore
Advances in Heavy-Tailed Sampling: Bridging Theory and Practice
Speakers
- Dr. Federica Milinanni KTH Royal Institute of Technology
- Dr. Ye He, Georgia Institute of Technology
- Dr. Chang-Han Rhee, Northwestern University
Chair/Organiser
- Dr. Alex Shestopaloff, Queen Mary University of London
- Dr. Jun Yang, University of Copenhagen
Advances in MCMC methods
Speakers
- Prof. Alexandros Beskos, University College London
- Prof. Antonietta Mira, Università della Svizzera italiana, Lugano and Insubria University, Como
- Dr. Pariya Behrouzi, Wageningen University and Research, The Netherlands
Chair/Organiser
- Prof. Maria De Iorio, National University of Singapore
Advances in Methodology for Sequential Monte Carlo in High Dimensions
Speakers
- Prof. Sumeetpal Singh, University of Wollongong
- Dr. Axel Finke, Loughborough University
- Prof. Victor Elvira, University of Edinburgh
Chair/Organiser
- Prof. Alexandros Beskos, University College London
Advances in Variational Inference
Speakers
- Dr. Jeremias Knoblauch, University College London
- Dr. Charles Margossian, Flatiron Institute
- Dr. Kamélia Daudel, ESSEC Business School
Chair/Organiser
- Prof. Randal Douc, Télécom Sud Paris
Approximate Methods for Accelerated Sampling
Speakers
- Prof. Debdeep Pati, University of Wisconsin-Madison
- Associate Prof. Yun Yang, University of Maryland, College Park
- Dr. Leo Duan, University of Florida
Chair/Organiser
- Asst. Prof. Yuexi Wang, University of Illinois Urbana-Champaign
Bayesian computation in astrophysics
Speakers
- Dr. Avi Vajpeyi , University of Auckland
- Dr. Alvin Chua, National University of Singapore
- Dr. Joshua S. Speagle, University of Toronto
Chair/Organiser
- Dr. Kate Lee, University of Auckland
Bayesian Federated Learning
Speakers
- Dr. Louis Aslett, University of Durham, UK
- Dr. Conor Hassan, Aalto University
- Prof Jean-Michel Marin, Universite de Montpellier
Chair/Organiser
- Prof. Christian Robert
Comparison theory for modern MCMC methods
Speakers
- Rocco Caprio, University of Warwick
- Dr. Guangyang Wang, University of Minnesota
- Associate Prof. Giacomo Zanella, Bocconi University
Chair/Organiser
- Dr. Andi Q. Wang, University of Warwick
Computation and model criticism for highly parametrized Bayesian models
Speakers
- Prof. Maria De Iorio, National University of Singapore
- Dr. Lucas Kock, National University of Singapore
- Prof. Michael Smith, University of Melbourne
Chair/Organiser
- Associate Prof. David Nott, National University of Singapore
Computation-enabled Bayesian inference and prediction
Speakers
- Asst. Prof. Edwin Fong, Department of Statistics, University of Hong Kong
- Asst. Prof. Yan Shuo Tan, Department of Statistics, National University of Singapore
- Asst. Prof. Naoki Awaya, Faculty of Political Science and Economics, Waseda University, Japan
Chair/Organiser
- Prof. Li Ma, Duke University, USA
Cutting Feedback: Methods and Applications in Bayesian Settings
Speakers
- Dr. Mikolaj Kasprzak, ESSEC Business School, Paris
- Associate Prof. David Nott, National University of Singapore
- Dr. Pantelis Samartsidis, MRC Biostatistics Unit, University of Cambridge
Discussant
- Prof. Daniela De Angelis, MRC Biostatistics Unit, University of Cambridge
Chair
- Prof. Daniela De Angelis, MRC Biostatistics Unit, University of Cambridge
Organisers
- Dr. Anne Presanis
Flexible approximate inference methods
Speakers
- Prof. Robert Kohn, University of New South Wales
- Dr. Linda Tan, National University of Singapore
- Dr. David Chen, National University of Singapore
Chair/Organiser
- Associate Prof. David Nott, National University of Singapore
High-dimensional discrete model search
Speakers
- Prof. Yves Atchadé, Boston University
- Dr. Hyunwoong Chang, University of Texas at Dallas
- Dr. Déborah Sulem, Universitá de la Svizzera Italiana
Chair/Organiser
- Prof. David Rossell, Pompeu Fabra University
Manifold Markov chain Monte Carlo and beyond
Speakers
- Dr. Mareike Hasenpflug, University of Passau
- Dr. Cameron Bell, University of Warwick
- Dr. Björn Sprungk, University of Freiberg
Chair/Organiser
- Prof. Daniel Rudolf, University of Passau
Model Misspecification in Simulation-based Inference
Speakers
- Associate Prof. François-Xavier Briol, University College London, UK
- Prof. Paul Bürkner, TU Dortmund University, Germany
- Dr. Antoine Wehenkel, Apple
Chair/Organiser
- Dr. Ayush Bharti, Aalto University, Finland
Optimization and Control for Sampling
Speakers
- Dr. Lorenz Richter, Zuse Institute Berlin
- Dr. Nikolas Nusken, King's College London
- Dr. Nikolay Malkin, University of Edinburgh
Chair/Organiser
- Associate Prof. Alexandre Thiery, National University of Singapore
Parallel Computations for Markov chain Monte Carlo
Speakers
- Prof. Dootika Vats, Department of Mathematics and Statistics, Indian Institute of Technology
- Asst. Prof. Lu Yu, Department of Data Science, City University of Hong Kong
- Dr. Sebastiano Grazzi, Department of Decision Sciences, Bocconi University
Chair
- Dr. Charles Margossian, Flatiron Institute, Simons Foundation
Organisers
- Dr. Sebastiano Grazzi
PDMPs in Practice
Speakers
- Prof. Kengo Kamatani, The Institute of Statistical Mathematics
- Mr. Luke Hardcastle, University College London
- Dr. Jere Koskela, University of Newcastle
Discussant
- Dr. Sebastiano Grazzi, Bocconi University
Chair
- Dr. Sebastiano Grazzi, Bocconi University
Organisers
- Mr. Luke Hardcastle
Recalibration methods for improved Bayesian inference from approximate models
Speakers
- Dr. Jeong E (Kate) Lee, University of Auckland
- Dr. Guilherme Souza Rodrigues, University of Brasilia
- Adam Bretherton, Queensland University of Technology
Discussant
- Prof. Scott Sisson, University of New South Wales
Chair/Organiser
- Dr. Joshua Bon, Université Paris Dauphine
Recent advances in Sequential Monte Carlo methods
Speakers
- Prof. Pierre Del Moral, INRIA Bordeaux
- Dr. Adrien Corenflos, University of Warwick
- Asst. Prof. Ning Ning, Texas A&M University
Chair/Organiser
- Dr. Hai-Dang Dau, National University of Singapore
Scalable Causal Inference
Speakers
- Prof. Michael J. Daniels, University of Florida
- Prof. Hui Guo, University of Manchester
- Dr. Jack Kuipers, ETH Zürich
Chair/Organiser
- Prof. Maria De Iorio, National University of Singapore
Simulation-based Bayesian inference: efficiency, robustness, and theoretical results
Speakers
- Prof. David Frazier, Monash University
- Asst. Prof. Yuexi Wang, University of Illinois Urbana-Champaign
- Ryan Kelly, PhD student, Queensland University of Technology
Discussant
- Dr. Clara Grazian, University of Sydney
Chair/Organiser
- Prof. Chris Drovandi, Queensland University of Technology
Surrogate Models and Kernel Methods
Speakers
- Prof. Dino Sejdinovic, University of Adelaide
- Dr. Karina Koval, Heidelberg University
- Dr. Aretha Teckentrup, University of Edinburgh
Chair/Organiser
- Prof. Chris. J. Oates, Newcastle University
Variational Bayes for Uncertainty Quantification
Speakers
- Associate Prof. Trevor Campbell, University of British Columbia
- Associate Prof. Tamara Broderick, Massachusetts Institute of Technology
- Dr. Yuling Yao, University of Texas, Austin
Chair/Organiser
- Dr. Ryan Giordano and Dr. Alex Strang, University of California, Berkeley
About Bayes Comp
The biennial Bayes Comp meetings are organised by the Bayesian Computation Section of the International Society for Bayesian Analysis. Bayes Comp 2025 is the fourth conference in the series and is hosted by the Department of Statistics and Data Science at the National University of Singapore.
The Bayesian approach to learning from data has a very long history, but it has only flourished in modern applications with the use of modern computational tools. Bayes Comp 2025 gives a snapshot of the current state of the diverse and exciting field of Bayesian computation.
Keynote Speakers
Pierre E. Jacob
Prof. Chris Oates, Newcastle University, UNITED KINGDOM
Sylvia Frühwirth-Schnatter
Prof. Christian Robert, Université Paris Dauphine, FRANCE University of Warwick, UNITED KINGDOM
Emtiyaz Khan
Prof. David T. Frazier, Monash University, AUSTRALIA
Organising Committee
Local organising committee
- David Nott (Chair) (NUS)
- Li Cheng (NUS)
- Michael Choi (NUS)
- Vik Gopal (NUS)
- Jeremie Houssineau (NTU)
- Maria De Iorio (NUS)
- Adrian Roellin (NUS)
- Linda Tan (NUS)
- Yan Shuo Tan (NUS)
- Alex Thiery (NUS)
- Xin Tong (NUS)
- Wu Zhengxiao (SMU)
Scientific program committee
- David Frazier and Leah South (Co-chairs)
- Pierre Alquier (ESSEC Business School)
- Taeryon Choi (Korea U)
- Francesca Crucinio (University of Torino and Collegio
Carlo Alberto) - Cathy Chen (Feng Chia U)
- David Gunawan (U of Wollongong)
- Kengo Kamatani (ISM)
- Kate Lee (Auckland)
- Kerrie Mengersen (QUT)
- Christian P Robert (Paris Dauphine/Warwick)
- Veronika Rockova (Chicago Booth)
- Judith Rousseau (Paris Dauphine/Oxford)
- Sumeetpal Singh (U of Wollongong)
- Mike So (HKUST)
- Dootika Vats (IIT Kanpur)
Respect Officers
ISBA has a code of conduct for meetings which can be found here In the event that you experience any harassment or violations of the code of conduct, you can approach one of the respect officers who have volunteered their help for the meeting. The respect officers for Bayes Comp 2025 are Kate Lee, Christian Robert and Linda Siew Li Tan, and they can be identified by the yellow straps for their registration tags. You may also approach any of the local organizers for assistance.
We thank the respect officers for their help, and hope everyone has a safe and enjoyable meeting.
Kate Lee
Christian Robert
Linda Siew Li Tan
Satellite Workshops
Bayesian Computation and Inference with Misspecified models
https://postbayes.github.io/BayesMisspecificationSatellite/A common justification for the use of Bayesian inference is that Bayes’ theorem is the optimal way to update beliefs based on new observations, and that representing beliefs through a posterior distribution is desirable for uncertainty quantification. However, standard posterior distributions are only meaningful when the model or likelihood is well-specified, which is not the case in the presence of outliers, adversarial contaminations, or faulty measurement instruments. This realisation has led to an increased focus on generalisations of Bayesian inference which aim at obtaining ‘generalised posterior distributions’ providing some representation of uncertainty but also overcoming some the lack of robustness of standard posteriors. The aim of this workshop will be to give a broad overview of this topic, touching on both foundational questions and algorithmic advances, and inviting the Bayesian Computation community to take a more active role in solving some of the remaining open challenges in this area.
Organisers
Associate Prof. François-Xavier Briol, University College London
Dr. Jack Jewson, Monash University
Dr Jeremias Knoblauch, University College London
Bayesian Methods for Distributional and Semiparametric Regression
https://kleinlab-statml.github.io/subpages_research/events/BayesComp2025.htmlThis satellite workshop aims to bridge the gap between computational and theoretical advancements and modern applications in Bayesian methods for distributional and semiparametric regression by bringing together leading experts in the field. Participants will benefit from talks that cover key tasks, such as model formulation, variable selection, inference techniques and associated computational challenges and practical implications. By highlighting the latest developments, this workshop will provide an overview of current research advancements, fostering discussions that inspire collaboration and innovation in advanced Bayesian regression.
Organisers
Prof. Nadja Klein, Scientific Computing Center, Karlsruhe Institute of Technology, Germany
Dr. Lucas Kock, Department of Statistics and Data Science, National University of Singapore,
Singapore
Presentation Instructions
Congratulations! Your abstract has been accepted!
Now it’s time to prepare your slides (Oral Session) or posters (Poster Session).
Read the instructions below to help you get ready!
(A) Oral Presentation
- The provisional session time slot is available at https://bayescomp2025.sg/#programme
- The finalised schedule will be announced closer to conference date.
- Please bring a copy of your presentation file on a USB memory stick to the venue in case it is lost or corrupted.
Session Chairs
Please arrive at the session you are chairing approximately 10 minutes prior to the start to make sure all the sessions presentations can be uploaded to the rooms IT facilities. Speakers will have the option to upload their slides - in pdf version - directly to the room's laptop (running Windows OS) or connect via their own device. As a default option, uploading all slides directly to the room's laptop would be safest, however, we understand that some speakers may want to use their own devices and are of course happy for you and your speakers to decide on how best to proceed.
Session times differ slightly across invited and contributed sessions.
- For invited sessions without a discussant, please keep each speaker to around 25 minutes so as to allow for audience Q&A at the end of the talk.
- For invited sessions with a discussant (taking place June 19 from 3.30-5.30), please keep each speaker to around 25 minutes to allow for a maximum 5 minute Q&A and to ensure the discussant has enough time to discuss the sessions contributions.
- For the "Parallel Contributed Paper Sessions (Day 1, 15.30-17.30)" session, please keep each speaker to around 15-18 minutes to allow for a 2-5 minute audience Q&A.
- For the contributed session on "Computationally Efficient Bayesian Inference (Day 3, 10.20- 11.50)", please ensure each presentation in the session is between around 25 minutes, to allow for a 5 minute audience Q&A.
The location and timing of your session will be available on the main conference website prior to the start of the conference. Thank you again for agreeing to chair a session at this year's conference.
Speakers in invited and contributed session
Please arrive at your session approximately 10 minutes prior to the start to ensure your presentations can be directly uploaded to the rooms presentation facilities. Speakers have the option to upload their slides - in pdf version - to the room's laptop (running Windows OS) or connect via their own device. When using the latter option, please ensure you bring an adapter capable of interfacing with an HDMI cable, which will be provided as part of each room's presentation facilities.
The location and timing of your session will be available on the main conference website prior to the start of the conference. Presentation lengths differ slightly depending on whether you are speaking in an invited or contributed session. The timing for each relevant permutation is as follows.
- If you are a speaker or discussant in an invited session, please ensure your presentation is around 25 minutes to allow for audience Q&A.
- If you are speaking in the "Parallel Contributed Paper Session (Day 1, 15.30- 17.30)", each speaker will have between 15-18 minutes to present their work, with a 2-5 minute Q&A to follow directly after each talk.
- If you are speaking in the contributed session on "Computationally Efficient Bayesian Inference (Day 3, 10.20- 11.50)", please ensure your presentation is around 25 minutes to allow for a five minute audience Q&A.
Thank you for participating in this year's conference.
Posters
Poster sessions will be run on the first two days of the main conference, with both sessions taking place from 17:30-19:30. Please ensure your poster is set up and manned during your appointed poster slot.
- Posters will be affixed using system panels, with the posteriors mounted on the panels via velcro tape. The velcro tape will be provided by the conference and can be obtained from the registration counter prior to your poster session.
- For the poster session taking place on 18 June 2025, 5.30pm, posters can be mounted anytime from 8:30 on 18 June 2025. Please make sure to remove your poster by 20:00 (8pm) at the end of the session to ensure we can ready the panels for the next session. Posters that are not removed by this time will be removed and disposed of.
- For the poster session taking place on 19 June 2025, 5.30pm, posters can be mounted anytime from 8:30 on 19 June 2025. Please make sure to remove your poster by 20:00 (8pm) at the end of the session. Posters that are not removed by this time will be removed and disposed of.
- Posters must be printed in A1 (594 x 841mm / 23.4 x 33.1in) dimensions and portrait mode only. Due to space constraints we cannot accept landscape formatted posters.
- Please print the posters on thick paper and avoid fabric if possible.
The poster session will take place in the covered walkway directly outside the main conference venue. Singapore is known to be humid, so please dress appropriately.
If you need assistance or have any questions, please contact us at ceuevents@nus.edu.sg
(B) Poster Presentation
A1 or smaller
Portrait Only
No Landscape
- Posters should be prepared and printed with final dimensions up to maximum
ISO A1 size. Printing services will not be available at the conference venue.
- Velcro will be provided.
- All posters must be in portrait, strictly no landscape format allowed.
- DO NOT USE FABRIC MATERIAL to print the posters
- You may choose to use the provided template (A1-size) attached here to prepare your poster:
- All presenters are requested to be at your poster to answer questions during
the Poster Session timings.
- 18 June 2025, 1730 – 1930hrs
Posters must be displayed from 0900 on 18 July 2025 and remove your poster by 2000 on 18 June 2025. The steering committee will dispose of any posters left after this time. - 19 June 2025, 1730 – 1930hrs
Posters must be displayed from 0900 on 19 July 2025 and remove your poster by 2000 on 19 June 2025. The steering committee will dispose of any posters left after this time.
- 18 June 2025, 1730 – 1930hrs
- Check your schedule and Poster locations here: https://bayescomp2025.sg/#poster-sessions
If you need assistance or have any questions, please contact us at ceuevents@nus.edu.sg
For printing options in Singapore (near campus), please consider the following options:
- Xorex Press (West Coast Plaza)
154 West Coast Road #02-30 West Coast Plaza, Singapore 127371
+65 6779 7989
https://maps.app.goo.gl/VXxq3ESCdbgJhJTc8?g_st=com.google.maps.preview.copy- Goh Bros E-Print Pte. Ltd. (Yusof Ishak House, NUS)
Yusof Ishak House, 31 Lower Kent Ridge Rd, Level 5 #YIH-05-01
National University of Singapore, Singapore 119078
+65 6776 2900
https://maps.app.goo.gl/GAbojsnrxVXS1UQA8- Green Prints Services
67 Ayer Rajah Crescent 07-10 Level, Take Lift, B 7, Singapore139950
+65 9757 8421
https://maps.app.goo.gl/pbRjawqLUUThwPNN7?g_st=com.google.maps.preview.copyPlease call and check the shop opening times before reaching.
Poster Sessions
|
18 Jun 2025, 5.30pm - 7.30pm |
|||
|---|---|---|---|
|
Poster |
Title |
Presenter |
|
|
A01 |
On A Modified Adaptive Progressive Censoring Scheme and Related Inferences |
Abhimanyu Singh Yadav |
Banaras Hindu University |
|
A02 |
Computationally Efficient Multi-Level Gaussian Process Regression for Functional Data Observed Under Completely Or Partially Regular Sampling Designs |
Adam Gorm Hoffmann |
University of Copenhagen |
|
A03 |
Advancing Estimation of Average Relative Humidity in The Usa Using Neutrosophic Stratified Ranked Set Sampling |
Anamika Kumari |
Manipal Academy of Higher Education |
|
A04 |
MCMC Importance Sampling via Moreau-Yosida Envelopes |
Apratim Shukla |
IIT Kanpur |
|
A05 |
Lower Bounds of Total Variation Distances for Multivariate Conditional Metropolis-Hastings Samplers |
Arka Banerjee |
IIT Kanpur |
|
A06 |
Generalized Exponential Proportional Hazard Model for Joint Modelling of Longitudinal and Survival Data |
Avinash Kumar |
Banaras Hindu University |
|
A07 |
Integrating Normative and Survival Modeling in MS via Bayesian Modularized Inference |
Bernd Taschler |
University of Oxford |
|
A08 |
Computational and Statistical Guarantees for Star-Structured Variational Inference |
Bohan Wu |
Columbia University |
|
A09 |
Particle-Based Inference for Continuous-Discrete State Space Models |
Christopher Stanton |
University College London |
|
A10 |
Look Ma, No Sampling! |
Colin Fox |
University of Otago |
|
A11 |
The ARR2 Prior: Flexible Predictive Prior Definition for Bayesian Auto-Regressions |
David Kohns |
Aalto University |
|
A12 |
Exact Sampling of Spanning Trees Via Fast-forwarded Random Walks |
Edric Tam |
Stanford University |
|
A13 |
Convergence of Statistical Estimators via Mutual information Bounds |
EL Mahdi Khribch |
Essec Business School |
|
A14 |
Proper Random Walks An Enhanced Approach To Robust Spline Smoothing |
Eman Kabbas |
King Abdullah University of Science and Technology |
|
A15 |
Calibration of Dose-Agnostic Priors for Bayesian Dose-Finding Trial Designs with Joint Outcomes |
Emily Alger |
Institute of Cancer Research |
|
A16 |
Extending Bayesian Causal forests for Longitudinal Data Analysis: A Case Study in Multiple Sclerosis |
Emma Prevot |
University of Oxford |
|
A17 |
Control Variate-Based Stochastic Sampling From The Probability Simplex |
Francesco Barile |
University of Milano-Bicocca |
|
A18 |
Zero-Order Parallel Sampling |
Francesco Pozza |
Bocconi University |
|
A19 |
Scalable MCMC Methods for Bayesian Blind Deconvolution |
Guillermina Senn |
Norges Teknisk-Naturvitenskapelige Universitet |
|
A20 |
Online Filtering for Discretely-Observed Diffusions with Blocked Particle Filters |
Hai-Dang Dau |
National University of Singapore |
|
A21 |
Orthogonal Polynomials Are All You Need: Skewed Posterior Approximations with Variational Bayes |
Hans Montcho |
King Abdullah University of Science and Technology |
|
A22 |
Causal Inference for Longitudinal Multilevel Data - A Bayesian Semiparametric G-Computation Approach |
Huixia Savannah Wang |
Umeå School of Business, Economics and Statistics |
|
A23 |
Enhanced Gaussian Process Surrogates for Optimization and Sampling By Pure Exploration |
Hwanwoo Kim |
Duke University |
|
A24 |
Mixtures of Directed Graphical Models for Discrete Spatial Random Fields |
J. Brandon Carter |
University of Texas At Austin |
|
A25 |
Bayesian Analysis of Clustered Data Within a Semi-Competing Risks Framework |
Jinheum Kim |
University of Suwon |
|
A26 |
Bayesian Robust Inference for Doubly-intractable Distributions via Score Matching |
Jiongran Wang |
Texas A&M University |
|
A27 |
On The forgetting of Particle Filters |
Joona Karjalainen |
University of Jyväskylä |
|
A28 |
Sampling from High-Dimensional, Multimodal Distributions Using Automatically Tuned, Tempered Hamiltonian Monte Carlo |
Joonha Park |
University of Kansas |
|
A29 |
Learning Misspecified Ode Models from Heterogeneous Data with Biology-informed Gaussian Processes |
Julien Martinelli |
Université De Bordeaux |
|
A30 |
Nonparametric Bayesian Additive Regression Trees for Prediction and Missing Data Imputation in Longitudinal Studies |
Jungang Zou |
Columbia University |
|
B31 |
Bayesian Combined Statistical Decision Limits with Covariates |
Lian Mae T. Tabien |
University of The Philippines Diliman |
|
B32 |
Robust and Conjugate Gaussian Process Regression |
Matias Altamiran |
University College London |
|
B33 |
Real-Time forecasting Livestock Disease Outbreaks with Approximate Bayesian Computation |
Meryl Theng |
The University of Melbourne |
|
B34 |
Bayesian Crossover Trial with Binary Data and Extension to Latin-Square Design |
Mingan Yang |
University of New Mexico, |
|
B35 |
The Polynomial Stein Discrepancy for Assessing Moment Convergence |
Narayan Srinivasan |
Queensland University of Technology |
|
B36 |
Improving Variable Selection Properties By Using External Data |
Paul Rognon-Vael |
Universitat Pompeu Fabra |
|
B37 |
Parallel Affine Transformation Tuning: Drastically Improving The Effectiveness of Slice Sampling |
Philip Schär |
Friedrich Schiller University Jena |
|
B38 |
A Simple Bayesian Solution to Reducing The Factor Zoo |
Robert I. Webb |
University of Virginia |
|
B39 |
Adaptive Shrinkage With A Nonparametric Bayesian Lasso |
Santiago Marin |
The Australian National University |
|
B40 |
A Spatial-correlated Multitask Linear Mixed-effects Model for Imaging Genetics |
Shufei Ge |
ShanghaiTech University |
|
B41 |
Iterated forward Scheme to Construct Proposals for Sequential Monte Carlo Algorithms |
Sylvain Procope-Mamert |
Université Paris-Saclay |
|
B42 |
Real-Time Estimation of Gas Emission Sources Using Particle Filters and Neural Networks |
Thomas Newman |
Lancaster University |
|
B43 |
Bayesian Computation for Partially Observed SPDEs |
Thorben Pieper-Sethmacher |
Delft University of Technology |
|
B44 |
A General Framework for Probabilistic Model Uncertainty |
Vik Shirvaikar |
University of Oxford |
|
B45 |
Bayesian Semiparametric Likelihood-Based Regression Inference for Optimal Dynamic Treatment Regimes |
Weichang Yu |
The University of Melbourne |
|
B46 |
Robust and Conjugate Spatio-Temporal Gaussian Processes |
William Laplante |
University College London |
|
B47 |
Information-Theoretic Classification of The Cutoff Phenomenon in Markov Processes |
Youjia Wang |
National University of Singapore |
|
B48 |
A Novel Approach for Forecasting Non-Stationary Time Series: Utilization of a Variational Autoencoder Reflecting Seasonal Patterns |
Young Eun Jeon |
andong National University |
|
B49 |
Robust Bayesian Methods Using Amortized Simulation-Based Inference |
Yuyan Wang |
National University of Singapore |
|
B50 |
A Framework for Measuring Dependence of Partitions On Covariates in Mixture Models |
Zhaoxi Zhang |
University of Edinburgh |
|
B51 |
Ensemble Filtering in Nonlinear Dynamical Systems: A Diffusion-based Approach |
Zhidi Lin |
National University of Singapore |
|
B52 |
Sample Continuation in Bayesian Hierarchical Model via Variational Inference |
Zilai Si |
Northwestern University |
|
B53 |
Nested Kernel Quadrature |
Zonghao Chen |
University College London |
|
19 Jun 2025, 5.30pm - 7.30pm |
|||
|---|---|---|---|
|
Poster |
Title |
Presenter |
|
| A01 | Challenges and Insights from Non-Uniform Polytope Sampling | A. Stratmann | Forschungszentrum Jülich |
| A02 | Bayesian Analysis of Historical Functional Linear Models | A.E. Clark | University of Cape Town |
| A03 | Dual Multi-Outcome Transformation Causal Estimation Biomarkers Discovery Framework Using DNA Methylation Against RNA and Proteins Expression | Ala’a El-Nabawy | Northumbria University |
| A04 | Mixing Time Bounds for The Gibbs Sampler Under Isoperimetry | Alexander Goyal | Imperial College London |
| A05 | Variational Bayes Inference for Simultaneous Autoregressive Models with Missing Data | Anjana Wijayawardhana | University of Wollongong |
| A06 | Approximating Bayesian Leave-One-Group-Out Cross-Validation | Anna Elisabeth Riha | Aalto University |
| A07 | Computationally Efficient Bayesian Joint Modeling of Mixed-Type High-Dimensional Multivariate Spatial Data | Arghya Mukherjee | IIT Kanpur |
| A08 | Decision Making Under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets | Charita Dellaporta | University of Warwick and University College London |
| A09 | Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes | Cheng Li | National University of Singapore |
| A10 | A More Consistent Approximate Bayesian Framework for Learning the Optimal Action-Value Function in MDPs | Chon Wai Ho | University of Cambridge |
| A11 | Bayesian Survival Model Updating Using Power Prior: Application to Cancer Data Analysis | Dahhay Lee | Yonsei University |
| A12 | Approximate Bayesian Fusion | Filippo Pagani | University of Warwick |
| A13 | The Spectrum of the Optimal Self-Regenerative and independent Metropolis Markov Chains with Applications to MCMC | Florian Maire | Université de Montréal |
| A14 | Detecting Conflicts in Bayesian Hierarchical Models Using Score Discrepancies | Fuming Yang | University of Cambridge |
| A15 | Decoding Socio-Economic inequalities in Uttar Pradesh: A Spatio-Temporal Study with Wroclaw Taxonomy and K-Means Clustering Techniques | Gaurav Chandrashekhar Hajare | Manipal Academy of Higher Education |
| A16 | Scalable Bayesian Factor Models for Dimensionality Reduction in High-Dimensional Multimodal Data with Structured Missingness | George Hutchings | University of Oxford |
| A17 | Simulation-Based Inference for Stochastic Nonlinear Mixed-Effects Models with Applications in Systems Biology | Henrik Häggström | Chalmers University of Technology and University of Gothenburg |
| A18 | Bayesian Inference of a Nearest Neighbor Gaussian Process Model for Pooled Genetic Data | Imke Botha | University of Melbourne |
| A19 | Branching Stein Variational Gradient Descent | Isaías Bañales | Kyoto University |
| A20 | Novel Bayesian Algorithms for ARFIMA Long-Memory Processes: A Comparison Between MCMC and ABC Approaches | James Gabor | University of Sydney |
| A21 | Scalable Bayesian Causal Inference for Uplift Modeling with Conformal Prediction | Jeong in Lee | Inha University |
| A22 | Pareto Smoothed ABC-SMC | Jia Le Tan | University of Warwick |
| A23 | Bayesian Neural Network Optimisation for Multi-Trait Parental Selection to Enhance Economic Gains in Animal and Plant Breeding | Jia Liu | Australian National University |
| A24 | Accelerating Bayesian Inference for Sequential Data Batches in Epidemic Transmission Models | Joel Kandiah | University of Cambridge |
| A25 | Reliable Chemical Toxicity Assessment Via Transformer Models and Conformal Prediction Methodology | Junhee Kim | Inha University |
| A26 | Post-Bayesian Inference for Misspecified Cosmological Models | Kai Lehman | Ludwig Maximilian University of Munich |
| A27 | A Data-Driven Approach To Bayesian Hierarchical Modelling and Bayesian Neural Networks for Critical Illness Risk Prediction | Kaitlyn Louth | University of Edinburgh and Heriot-Watt University, |
| A28 | A Formal Method for Verifying Bayes Factor Computations Using Half-Order Moments | Kensuke Okada | The University of Tokyo |
| B29 | Symmetrizing Variational Monte Carlo Solvers for The Many-Electron Schrödinger Equation | Kevin Han Huang | University College London |
| B30 | Validation of Bayesian Population and Sub-Population Estimates | Lauren Kennedy | University of Adelaide |
| B31 | Ensemble Control Variates | Long M. Nguyen | Queensland University of Technology |
| B32 | Bayesian Perspectives on Data Augmentation for Deep Learning | Madi Matymov | King Abdullah University of Science and Technology |
| B33 | Cohering Disaggregation and Uncertainty Quantification for Spatially Misaligned Data | Man Ho Suen | University of Edinburgh |
| B34 | Scaling Laws for Uncertainty in Deep Learning | Mattia Rosso | King Abdullah University of Science and Technology |
| B35 | GANs Secretly Perform Approximate Bayesian Model Selection | Maurizio Filippone | King Abdullah University of Science and Technology |
| B36 | Projected and Updated L0 Criteria for Variable Selection in High-Dimensional and Large-Sample Regression Models | Maxim Fedotov | Universitat Pompeu Fabra |
| B37 | Bayesian Ranking of Treatments for Static Evaluation and Adaptive intervention | Miguel R. Pebes-Trujillo | Nanyang Technological University |
| B38 | Variable Selection and Estimation Using Nonlocal Prior Mixtures for Data with Widely Varying Effect Sizes | Nilotpal Sanyal | University of Texas at El Paso |
| B39 | Adversarial Robustification of Bayesian Prediction Models | Pablo García Arce | Instituto de Ciencias Matemáticas |
| B40 | Deterministic Posterior Approximations in Streaming Data Scenarios | Patric Dolmeta | Universita' di Torino |
| B41 | Bayesian Analysis of Cumulative Damage Models with Continuous Damage Functions | Rijji Sen | University of Calcutta |
| B42 | Creating Rejection-Free Samplers By Rebalancing Skew-Balanced Jump Processes | Ruben Seyer | Chalmers University of Technology and University of Gothenburg |
| B43 | Exploring Bimodal Fertility Patterns: A Bayesian Mixture Density Approach | Shambhavi Singh | Banaras Hindu University |
| B44 | Approximate Maximum Likelihood Estimation with Local Score Matching | Sherman Khoo | University of Bristol |
| B45 | Digital Biomarker Construction Via Bayesian Motif-Based Clustering Method of Freeliving Physical Activity Data From Wearable Devices | Sin-Yu Su | National Taiwan University |
| B46 | Learning The Learning Rate in Generalized Bayesian Inference | Sitong Liu | University of Oxford |
| B47 | BMW: Inlier Prone Bayesian Models for Correlated Bivariate Data | Sumangal Bhattacharya | Indian Statistical Institute Delhi |
| B48 | AI-Powered Bayesian Inference | Veronika Rockova | University of Chicago |
| B49 | Bayesian Dynamic Generalized Additive Model for Mortality During COVID-19 Pandemic | Wei Zhang | Bocconi University |
| B50 | Localized Transfer Learning in Non-Stationary Spatial Model with PM2.5 Data | Wenlong Gong | University of Houston System |
| B51 | Predictive Performance of Power Posteriors | Yann McLatchie | University College London |
| B53 | Measure Transport with Kernel Mean Embeddings | Linfeng Wang | King's College London |
| B54 | Bayesian Empirical Likelihood with Expectation-Propagation | Kenyon Ng | Monash University |
Registration Fees
| Categories | Regular registration (30 March - 15 June) |
|
|---|---|---|
| Main Conference (16 - 20 June 2025) (Does not include access to Satellite Workshops) | ||
| Non-Students | Member of ISBA | S$710 |
| Member of SBSS | S$850 | |
| Non-member of ISBA/SBSS | S$930 | |
| Students | Member of ISBA | S$460 |
| Member of SBSS | S$500 | |
| Non-member of ISBA/SBSS | S$530 | |
| Satellite Workshops (16 - 20 June 2025) (Does not include access to Main Conference) | ||
| Non-Students | Member of ISBA | S$460 |
| Member of SBSS | S$520 | |
| Non-member of ISBA/SBSS | S$600 | |
| Students | Member of ISBA | S$220 |
| Member of SBSS | S$260 | |
| Non-member of ISBA/SBSS | S$300 | |
| Bayes Comp NUS Student Hostel Package (15 June - 21 June) (Limited rooms available) |
S$260 for Student Hostel Package (5 days, 4
nights) S$370 for Student Hostel Package (7 days, 6 nights) NUS University Town (UTown), Kent Ridge Campus Register by 16 May 2025 |
|
| Hotels (within 10km of NUS) Direct booking with the hotel. Refer to link. (Breakfast to be purchased at the front desk) |
S$145++ to S$195++ Per Night • Park Avenue Rochester • Citadines Science Park (Key in "BAYESCOMP25" under Promotion field to enjoy conference rates) |
|
NUS UTown Student Hostels
Standard Rooms (Air-conditioned)
A single-person living space furnished with a single bed, a ceiling fan, a writing desk and chair, a bookshelf, a wardrobe and a mobile pedestal. Shower and toilet facilities as well as kitchenettes (at selected levels) are located along the common corridors or within the apartments. Kitchenettes are equipped with stoves and other kitchen appliances where residents can cook their own meals instead of eating out.
-
Student Hostel Rooms at NUS UTown, Kent Ridge Campus:
- As our hostels are gender specific, please indicate your birth gender during registration.
- If you are planning to check-in later than 15 June, please indicate in remarks when selecting your housing option.
- There will be no refund or discounted rate for late check-in or early check-out.
- Registration will close on 16 May 2025.
Our budget-friendly accommodation option offers single occupancy rooms with shared bathroom facilities and come with single bed, one pillow, one blanket, one towel, a set of basic toiletries, a refillable water bottle and free wi-fi.
Off-Campus Accommodation
- Park Avenue Rochester
- Citadines Science Park Singapore
Lyf one-north
8 mins to Utown
Park Avenue Rochester
8 mins to Utown
Citadines Science Park Singapore
9 mins to Utown
Sentosa
Marina Bay Sands
Gardens by the Bay
Changi Airport
Chinese Garden
Mandai Wildlife Reserve
Singapore Botanic Gardens
Katong
Orchard Road
Park Avenue Rochester
(8 min to UTown)
Superior King
Superior Double
*To be purchased at the front desk
Superior King
Superior Double
Citadines Science Park
(9 min to UTown)
Studio Twin
Studio Executive - King Bed
1 Bedroom Executive
w/ Fully equipped Kitchenette w/ Washer & Dryer
Wifi included
Key in "BAYESCOMP25" under Promotion field to enjoy conference rates
Junior Travel Support
Bayes Comp 2025 will offer partial support for travel of selected PhD students and junior researchers who are presenting talks or posters to attend the meeting. This partial support is made possible by sponsorship from the International Society for Bayesian Analysis (ISBA) and the Bayesian Computation Section of ISBA
The amount of support will be up to USD250. Reimbursement will occur after the meeting, and receipts will be required. The instructions for how to obtain reimbursement will be shared with the successful applicants
Childcare Support
Bayes Comp 2025 will offer partial support for selected PhD students and junior researchers with young children (less than 13 years old) attending the meeting. This partial support is made possible by sponsorship from the International Society for Bayesian Analysis (ISBA) and the Bayesian Computation Section of ISBA
The amount of support will be up to USD250. Reimbursement will occur after the meeting, and receipts will be required to substantiate the amount spent on childcare. The instructions for how to obtain reimbursement will be shared with the successful applicants.
Contact Us
For general enquiries, please email to stabox20@nus.edu.sg
If you have questions regarding your registration and ticketing, please send an email to ceuevents@nus.edu.sg
Sponsored by
Organised by
© 2025 Bayes Comp 2025. All Rights Reserved. National University of Singapore.
For enquiries, please email to bayescomp2025@nus.edu.sg
Emtiyaz Khan (also known as Emti) is a (tenured) team leader at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where he leads the Approximate Bayesian Inference Team. Previously, he was a postdoc and then a scientist at Ecole Polytechnique Fédérale de Lausanne (EPFL), where he also taught two large machine learning courses and received a teaching award. He finished his PhD in machine learning from University of British Columbia in 2012. The main goal of Emti’s research is to understand the principles of learning from data and use them to develop algorithms that can learn like living beings. For more than 10 years, his work has focused on developing Bayesian methods that could lead to such fundamental principles. The approximate Bayesian inference team now continues to use these principles, as well as derive new ones, to solve real-world problems.
Pierre E. Jacob is a professor of statistics at ESSEC Business School, in Paris, France. He studies Bayesian inference and its variants as well as Monte Carlo algorithms. Pierre received his PhD from Université Paris Dauphine in 2012, and became a postdoc at the National University of Singapore and at the University of Oxford. He was a tenure-track faculty in the Department of Statistics at Harvard University between 2015 and 2021. He received the Guy Medal in Bronze from the Royal Statistical Society (2021), the COPSS Leadership Academy award (2022), and gave the Susie Bayarri lecture at the ISBA World Meeting in 2022. Pierre is also an associate editor of the Journal of the Royal Statistical Society: Series B since 2024.
Sylvia Frühwirth-Schnatter is Professor of Applied Statistics and Econometrics at the Department of Finance, Accounting, and Statistics at WU Vienna University of Economics and Business (Austria). Her research has been published in many leading journals and focuses on latent variable models, including finite mixture models, factor models and state space models and their estimation in a Bayesian framework using efficient sampling techniques. She is particularly interested in applying Bayesian inference in economic and financial time series analysis and in causal inference in micro-economics. She served the Bayesian community in many roles, including ISBA President, Chair of the ISBA Section on Economics, Finance and Business, and Co-Chair of the European Seminar of Bayesian Econometrics. She is elected Member of the Austrian Academy of Sciences and was awarded the 2007 Morris-De-Groot Price for her book on Finite Mixture and Markov Switching Models and the 2024 Zellner Medal by ISBA.