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Factorial Hidden Markov Models (FHMMs) are powerful models for sequentialdata but they do not scale well with long sequences. We propose a scalableinference and learning algorithm for FHMMs that draws on ideas from thestochastic variational inference, neural network and copula literatures. Unlikeexisting approaches, the proposed algorithm requires no message passingprocedure among latent variables and can be distributed to a network ofcomputers to speed up learning. Our experiments corroborate that the proposedalgorithm does not introduce further approximation bias compared to the provenstructured mean-field algorithm, and achieves better performance with longsequences and large FHMMs.

Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages