Parameter Estimation of General HSMM
Abstract
This chapter discusses the maximum-likelihood estimation of model parameters for the general HSMM, and uses the theory associated with the well-known EM (expectation-maximization) algorithm to prove that the parameter estimation procedure for the general HSMM increases the likelihood function and converges to the maximum. The formulas and algorithms for unsupervised, supervised, or semi-supervised learning of model parameters are derived for the cases when there are one or multiple observation sequences. An order estimation method for the general HSMM is provided. Finally, approaches for online update of model parameters are yielded based on forward-only algorithm or maximization of the likelihood ...
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