Title: Approximate Collapsed Gibbs Clustering with Expectation Propagation,
Abstract: We develop a framework for approximating collapsed Gibbs sampling in
generative latent variable cluster models. Collapsed Gibbs is a popular MCMC
method, which integrates out variables in the posterior to improve mixing.
Unfortunately for many complex models, integrating out these variables is
either analytically or computationally intractable. We efficiently approximate
the necessary collapsed Gibbs integrals by borrowing ideas from expectation
propagation. We present two case studies where exact collapsed Gibbs sampling
is intractable: mixtures of Student-t's and time series clustering. Our
experiments on real and synthetic data show that our approximate sampler
enables a runtime-accuracy tradeoff in sampling these types of models,
providing results with competitive accuracy much more rapidly than the naive
Gibbs samplers one would otherwise rely on in these scenarios.