Construction of Dependent Dirichlet Processes based on Poisson Processes

Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)

Bibtex Metadata Paper Supplemental

Authors

Dahua Lin, Eric Grimson, John Fisher

Abstract

We present a novel method for constructing dependent Dirichlet processes. The approach exploits the intrinsic relationship between Dirichlet and Poisson pro- cesses in order to create a Markov chain of Dirichlet processes suitable for use as a prior over evolving mixture models. The method allows for the creation, re- moval, and location variation of component models over time while maintaining the property that the random measures are marginally DP distributed. Addition- ally, we derive a Gibbs sampling algorithm for model inference and test it on both synthetic and real data. Empirical results demonstrate that the approach is effec- tive in estimating dynamically varying mixture models.