HMM (Hidden Markov Model) is a toolbox for inferring and learning from discrete outputs (dhmm's), Gaussian outputs (ghmm's), or mixtures of Gaussians output (mhmm's). It supports discrete inputs, such as those used in POMDPs. Inference routines include filtering, smoothing, and fixed-lag smoothing.
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