Hidden Markov models have become a widely used class of statistical models with applications in diverse areas such as communications engineering, bioinformatics, finance and many more. This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states.
In a unified way the book covers both models with finite state spaces, which allow for exact algorithms for filtering,...
Hidden Markov models have become a widely used class of statistical models with applications in diverse areas such as communications engineering, b...
Hidden Markov models have become a widely used class of statistical models with applications in diverse areas such as communications engineering, bioinformatics, finance and many more. This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states.
In a unified way the book covers both models with finite state spaces, which allow for exact algorithms for filtering,...
Hidden Markov models have become a widely used class of statistical models with applications in diverse areas such as communications engineering, b...
