| CARVIEW |
Dennis Prangle's home page
I’m an associate professor in statistics at the University of Bristol.
My current research is on the interface between Bayesian statistics and machine learning. I am particularly interested in developing approximate inference methods such as simulation based inference approaches, variational inference and composite likelihood. One application is to likelihood-free inference, where simulation of data is possible but the likelihood function is unavailable. Another is to stochastic differential equations I’ve worked on applications to population genetics, physics, ecology and epidemiology. I’m also interested in experimental design and how to quickly derive effective high dimensional designs.
Resources
Blog posts list
- 31 Dec 2022 » Revisiting ABC posterior convergence
- 14 Nov 2019 » Review of ABC talk
- 07 Sep 2019 » High dimensional Bayesian experimental design - part II
- 31 Aug 2019 » High dimensional Bayesian experimental design - part I
- 04 Aug 2019 » Bibtex tips
- 12 May 2019 » Posters in LaTeX
- 28 Apr 2019 » Mailing lists
- 07 Jun 2016 » Bayesian inference by neural networks. Part 2: new paper
- 07 Jun 2016 » Bayesian inference by neural networks. Part 1: background
- 17 Jan 2016 » Jupyter and R basics
- 03 Jan 2016 » Likelihood-free timeline
- 27 Sep 2015 » My work software list
- 20 Sep 2015 » Newcastle staff email on thunderbird
© 2025 Dennis Prangle with help from Jekyll Bootstrap and Bootstrap