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Construction of Dependent Dirichlet Processes based on Poisson Processes
Dahua Lin · Eric Grimson · John Fisher III

Wed Dec 08 12:00 AM -- 12:00 AM (PST) @ None #None

We present a method for constructing dependent Dirichlet processes. The new approach exploits the intrinsic relationship between Dirichlet and Poisson processes 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, removal, and location variation of component models over time while maintaining the property that the random measures are marginally DP distributed. Additionally, 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 effective in estimating dynamically varying mixture models.

Author Information

Dahua Lin (Massachusetts Institute of Technology)
Eric Grimson (Massachusetts Institute of Technology)
John Fisher III (MIT)

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