This code corresponds to our UAI 2021 paper. In this paper, we provide an efficient streaming inference algorithm for infinite mixture models using a novel Bayesian recursion on the Chinese Restaurant Process.

To make streaming inference possible, we break the CRP's conditional distribution $$p(z_t|z_{<t}, \alpha)$$ on the complete history by replacing it with a marginal distribution $$p(z_t|\alpha)$$. The running sum of the previous marginal distributions $$\sum_{t' < t} p(z_{t'} = k)$$ (left) and the Chinese restaurant table distribution $$p(K_{t-1} = k)$$ (middle) compete to determine the next marginal distribution $$p(z_t = k)$$ (right). Note the logarithmic scaling.

After cloning the repository, create a virtual environment for Python 3:

python3 -m venv rcrp

Then activate the virtual environment:

source rcrp/bin/activate

Ensure pip is up to date:

pip install --upgrade pip

Then install the required packages:

pip install -r requirements.txt

We did not test Python2, but Python2 may work.

Each experiment has its own directory, each containing a main.py that creates a plots subdirectory (e.g. exp_00_crp_prior/plots) and then reproduces the plots in the paper. Each main.py should be run from the repository directory e.g.:

python3 exp_00_crp_prior/main.py

Questions? Comments? Interested in collaborating? Open an issue or email Rylan Schaeffer at rylanschaeffer@gmail.com and cc Ila Fiete at fiete@mit.edu.