Model Parameter Estimation
作者： William Turin
DOI: 10.1007/978-1-4419-9070-9_4
关键词: Hidden Markov model 、 Forward algorithm 、 Algorithm 、 Computer science 、 Parameter identification problem 、 Stochastic process 、 Free parameter 、 Expectation–maximization algorithm 、 Maximization 、 Curve fitting
摘要: In this chapter, we develop methods for approximating a stochastic process with an HMM and, in particular, fitting HMMs to experimental data. We present the iterativeexpectation maximization(EM) algorithm approximation which generalizes statistical EM theBaum-Welch algorithm(BWA) HMM. This generalized can be also applied curve and finding maximum of nonnegative function several variables. The is iterative converges slowly. Therefore, it important select good initial model. describe choosing question speeding up convergence addressed.
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