Construction of Dependent Dirichlet Processes Based on Poisson Processes
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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Neural Information Processing Systems Foundation (NIPS)
2012
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| Online Access: | http://hdl.handle.net/1721.1/73948 https://orcid.org/0000-0003-4844-3495 https://orcid.org/0000-0002-6192-2207 |
| Summary: | 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. |
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