Learning Heterogeneous Hidden Markov Random Fields.
Ontology highlight
ABSTRACT:
SUBMITTER: Liu J
PROVIDER: S-EPMC4232933 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
Publications
Learning Heterogeneous Hidden Markov Random Fields.
Liu Jie J Zhang Chunming C Burnside Elizabeth E Page David D
JMLR workshop and conference proceedings 20140101
Hidden Markov random fields (HMRFs) are conventionally assumed to be homogeneous in the sense that the potential functions are invariant across different sites. However in some biological applications, it is desirable to make HMRFs heterogeneous, especially when there exists some background knowledge about how the potential functions vary. We formally define heterogeneous HMRFs and propose an EM algorithm whose M-step combines a contrastive divergence learner with a kernel smoothing step to inco ...[more]
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