Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages

Part of Advances in Neural Information Processing Systems 29 (NIPS 2016)

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Yin Cheng Ng, Pawel M. Chilinski, Ricardo Silva


Factorial Hidden Markov Models (FHMMs) are powerful models for sequential data but they do not scale well with long sequences. We propose a scalable inference and learning algorithm for FHMMs that draws on ideas from the stochastic variational inference, neural network and copula literatures. Unlike existing approaches, the proposed algorithm requires no message passing procedure among latent variables and can be distributed to a network of computers to speed up learning. Our experiments corroborate that the proposed algorithm does not introduce further approximation bias compared to the proven structured mean-field algorithm, and achieves better performance with long sequences and large FHMMs.