Inference in Hidden Markov Models

This is the web page for the book of the same name authored by Olivier Cappé, Eric Moulines, and Tobias Rydén, published by Springer in July 2005. The publisher's web page for the book is there.

From here, you will find pointers to the table of contents and a somewhat expanded version of the same thing (with the preface and the first page of each chapter), as well as the book cover (most useless except for fans who want to print T-shirts!) To our greatest shame, there is also a list of known errors which includes a corrected index (our most significant erratum to date). In the future, we also expect to make available some bibliographic material related to HMMs the matlab/octave code used to implement some of the algorithms described in the book.

MathSciNet features a complete review of the book. The book was also reviewed in the August 2006 issue of the ISI Short Book Reviews

and in the November 2006 issue of Technometrics. Google has a page on the book from which you can search terms inside the text (note that the effectiveness of this feature is greatly reduced by the fact that the pages in the central part of the book were scanned upside-down!) Below is what the backcover says about the book.

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, estimation etc., and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Simulation in hidden Markov models is addressed in five different chapters which cover both Markov chain Monte Carlo and sequential Monte Carlo approaches. Many examples illustrate the algorithms and theory. The book also carefully treats Gaussian linear state-space models and their extensions and it contains a chapter on general Markov chain theory and probabilistic aspects of hidden Markov models.
This volume will suit anybody with an interest in inference for stochastic processes, and it is meant to be useful for researchers and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more. The algorithmic parts of the book do not require an advanced mathematical background, while the more theoretical parts require knowledge of probability theory at the measure-theoretical level.
Olivier Cappé is Researcher for the French National Center for Scientific Research (CNRS). He received the Ph.D. degree in 1993 from Ecole Nationale Supérieure des Télécommunications, Paris, France, where he is currently a Research Associate. Most of his current research concerns computational statistics and statistical learning.
Eric Moulines is Professor at Ecole Nationale Supérieure des Télécommunications (ENST), Paris, France. He graduated from Ecole Polytechnique, France, in 1984 and received the Ph.D. degree from ENST in 1990. He has authored more than 150 papers in applied probability, mathematical statistics and signal processing.

Tobias Rydén is Professor of Mathematical Statistics at Lund University, Sweden, where he also received his Ph.D. in 1993. His publications include papers ranging from statistical theory to algorithmic developments for hidden Markov models.