Hidden Markov Models : Theory and Implementation using MATLAB® (Hardcover)

Introduction System models Markov chains Book outline 　 Probability Theory and Stochastic Processes Introduction Introduction to probability theory Probability density function Statistical moments Summary 　 Discrete Hidden Markov Models Introduction Hidden Markov model dynamics Probability transitions estimation Viterbi training algorithm Gradient-based algorithms Architectures for Markov models Summary 　 Continuous hidden Markov models Introduction Probability density functions and Gaussian mixtures Continuous hidden Markov model dynamics Continuous observations Baum-Welch training algorithm Summary 　 Autoregressive Markov models Introduction ARMM structure Likelihood and probability density for AR models Likelihood of an ARMM ARMM parameters estimations Time series prediction with ARMM Summary 　 Selected Applications Cardiotocography classification Solar radiation prediction Summary 　 Glossary 　 References Index