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Stochastic variational inference for hidden Markov models
Nick Foti · Jason Xu · Dillon Laird · Emily Fox

Thu Dec 11 11:00 AM -- 03:00 PM (PST) @ Level 2, room 210D

Variational inference algorithms have proven successful for Bayesian analysis in large data settings, with recent advances using stochastic variational inference (SVI). However, such methods have largely been studied in independent or exchangeable data settings. We develop an SVI algorithm to learn the parameters of hidden Markov models (HMMs) in a time-dependent data setting. The challenge in applying stochastic optimization in this setting arises from dependencies in the chain, which must be broken to consider minibatches of observations. We propose an algorithm that harnesses the memory decay of the chain to adaptively bound errors arising from edge effects. We demonstrate the effectiveness of our algorithm on synthetic experiments and a large genomics dataset where a batch algorithm is computationally infeasible.

Author Information

Nick Foti (Apple & University of Washington)
Jason Xu (University of California Los Angeles (UCLA))
Dillon Laird (University of Washington)
Emily Fox (Stanford University)

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