hsmm - An R package for analyzing hidden semi-Markov models
Computational Statistics & Data Analysis
A survey of techniques for incremental learning of HMM parameters
Information Sciences: an International Journal
Identifying anomalous signals in GPS data using HMMs: An increased likelihood of earthquakes?
Computational Statistics & Data Analysis
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The parameters of a hidden Markov model (HMM) can be estimated by numerical maximization of the log-likelihood function or, more popularly, using the expectation---maximization (EM) algorithm. In its standard implementation the latter is unsuitable for fitting stationary hidden Markov models (HMMs). We show how it can be modified to achieve this. We propose a hybrid algorithm that is designed to combine the advantageous features of the two algorithms and compare the performance of the three algorithms using simulated data from a designed experiment, and a real data set. The properties investigated are speed of convergence, stability, dependence on initial values, different parameterizations. We also describe the results of an experiment to assess the true coverage probability of bootstrap-based confidence intervals for the parameters.