Name
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. |
Year | Venue | Field |
|---|---|---|
2010 | NIPS | Hierarchical Dirichlet process,Latent Dirichlet allocation,Model inference,Computer science,Markov chain,Artificial intelligence,Dirichlet distribution,Poisson distribution,Machine learning,Gibbs sampling,Mixture model |
DocType | Citations | PageRank |
Conference | 8 | 0.64 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dahua Lin | 1 | 1117 | 72.62 |
| W. E. L. Grimson | 2 | 11451 | 2002.95 |
| John W. Fisher III | 3 | 878 | 74.44 |
| Fisher, J.W. | 4 | 542 | 55.82 |