Abstract
We present products of hidden Markov models (PoHMM's), a way of
combining HMM's to form a distributed state time series model. Inference in a PoHMM
is tractable and efficient. Learning of the parameters, although intractable, can be
effectively done using the Product of Experts Learning rule. The distributed state
helps the model to explain data which has multiple causes, and the fact that each model
need only explain part of the data means a PoHMM can capture longer range structure than
an HMM is capable of. We show some results on modelling character strings, a simple
language task and the symbolic family trees problem, which highlight these advantages.
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