Inference in Hidden Markov Model.pdf

Springer Series in Statistics
Advisors:
P. Bickel, P. Diggle, S. Fienberg, U. Gather,
I. Olkin, S. Zeger
Olivier Cappe´
Eric Moulines
Tobias Ryde´n
Inference in Hidden
Markov Models
With 78 Illustrations
Olivier Cappe´ Eric Moulines
CNRS RS LTCI
GET/Te´ Paris GET/Te´ Paris
46 rue Barrault 46 rue Barrault
75634 Paris cedex 13 75634 Paris cedex 13
France France
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Tobias Ryde´n
Centre for Mathematical Sciences
Lund University
Box 118
221 00 Lund
Sweden
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Library of Congress Control Number: 2005923551
ISBN-10: 0-387-40264-0 Printed on acid-free paper.
ISBN-13: 978-0387-40264-2
© 2005 Springer Science+Business Media, Inc.
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987654321
Preface
Hidden Markov models—most often abbreviated to the acronym “HMMs”—
are one of the most essful statistical modelling ideas that have came up in
the last forty years: the use of hidden (or unobservable) states makes the model
generic enough to handle a variety plex real-world time series, while the
relatively simple prior dependence structure (the “Markov” bit) still allows
for the use of putational procedures. Our goal with this book is to
present a plete picture of statis