Palla, K and Knowles, DA and Ghahramani, Z (2014) *A reversible infinite HMM using normalised random measures.* 31st International Conference on Machine Learning, ICML 2014, 5. pp. 4090-4107.

## Abstract

Copyright 2014 by the author(s). We present a nonparametric prior over reversible Markov chains. We use completely random measures, specifically gamma processes, to construct a countably infinite graph with weighted edges. By enforcing symmetry to make the edges undirected we define a prior over random walks on graphs that results in a reversible Markov chain. The resulting prior over infinite transition matrices is closely related to the hierarchical Dirichlet process but enforces reversibility. A reinforcement scheme has recently been proposed with similar properties, but the de Finetti measure is not well characterised. We take the alternative approach of explicitly constructing the mixing measure, which allows more straightforward and efficient inference at the cost of no longer having a closed form predictive distribution. We use our process to construct a reversible infinite HMM which we apply to two real datasets, one from epigenomics and one ion channel recording.

Item Type: | Article |
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Subjects: | UNSPECIFIED |

Divisions: | Div F > Computational and Biological Learning |

Depositing User: | Cron Job |

Date Deposited: | 17 Jul 2017 19:45 |

Last Modified: | 03 Aug 2017 03:15 |

DOI: |