Average Reviews:
(More customer reviews)This book is more about measure theory and pure statistical properties. For me (as a practicer), I found it's difficult to
extract important information for the book.
My biggest complain is in first few chapters, the authers simply
list all definition, properties and proofs without a single example
to help readers understand. And the notations are quite complicated which gives readers no clue if one reads directly from the later chapters (more algorithms involved).
Through out the entire book, I can't find any complete and concrete
applications. The bottomline is this book is neither practical
nor can serve a textbook to understand fundamental statistical theories.
Click Here to see more reviews about: Inference in Hidden Markov Models (Springer Series in Statistics)
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 and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Many examples illustrate the algorithms and theory. This book builds on recent developments to present a self-contained view.
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