Speeding up Gibbs sampling by variable grouping

Details

Event NIPS Workshop on Applications for Topic Models: Text and Beyond
Technical Publications
December 11th 2009
Gibbs sampling is a widely applicable inference technique that can in principle deal with complex multimodal distributions. Unfortunately, it fails in many practical applications due to slow convergence and abundance of local minima. In this paper, we propose a general method of speeding up Gibbs sampling in probabilistic models. The method works by introducing auxiliary variables which represent assignments of the original model variables to groups. Our experiments indicate that the groups converge early in the sampling. After they have converged, the original variables no longer need to be sampled, and it becomes possible to resample an entire group at a time, greatly speeding up the sampler. The proposed ideas are illustrated on two different topic models.

Citation

Bart, E. Speeding up Gibbs sampling by variable grouping. NIPS Workshop on Applications for Topic Models: Text and Beyond; 2009 December 11; Whistler, Canada.

Additional information

Focus Areas

Our work is centered around a series of Focus Areas that we believe are the future of science and technology.

FIND OUT MORE
Licensing & Commercialization Opportunities

We’re continually developing new technologies, many of which are available for¬†Commercialization.

FIND OUT MORE
News

PARC scientists and staffers are active members and contributors to the science and technology communities.

FIND OUT MORE