Semi-Markov Chains and Hidden Semi-Markov Models Toward Applications

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Format: Paperback
Pub. Date: 2008-08-15
Publisher(s): Springer Verlag
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Summary

This book is concerned with the estimation of discrete-time semi-Markov and hidden semi-Markov processes. Semi-Markov processes are much more general and better adapted to applications than the Markov ones because sojourn times in any state can be arbitrarily distributed, as opposed to the geometrically distributed sojourn time in the Markov case. Another unique feature of the book is the use of discrete time, especially useful in some specific applications where the time scale is intrinsically discrete. The models presented in the book are specifically adapted to reliability studies and DNA analysis. The book is mainly intended for applied probabilists and statisticians interested in semi-Markov chains theory, reliability and DNA analysis, and for theoretical oriented reliability and bioinformatics engineers. It can also serve as a text for a six month research-oriented course at a Master or PhD level. The prerequisites are a background in probability theory and finite state space Markov chains.

Author Biography

Nikolaos Limnios is a professor in Applied Mathematics at the University of Technology of Compiegne.

Table of Contents

Prefacep. VII
Introductionp. 1
Object of the Studyp. 1
The Underlying Idea in Semi-Markov Modelsp. 1
Discrete Timep. 4
Discrete-Time Semi-Markov Frameworkp. 5
Discrete-Time Renewal Processesp. 5
Semi-Markov Chainsp. 7
Semi-Markov Chain Estimationp. 10
Reliability Theory of Discrete-Time Semi-Markov Systemsp. 11
Hidden Semi-Markov Modelsp. 13
Discrete-Time Renewal Processesp. 17
Renewal Chainsp. 18
Limit Theoremsp. 26
Delayed Renewal Chainsp. 31
Alternating Renewal Chainp. 37
Exercisesp. 40
Semi-Markov Chainsp. 43
Markov Renewal Chains and Semi-Markov Chainsp. 44
Markov Renewal Theoryp. 51
Limit Theorems for Markov Renewal Chainsp. 60
Periodic SMCp. 67
Monte Carlo Methodsp. 67
Example: a Textile Factoryp. 68
Exercisesp. 73
Nonparametric Estimation for Semi-Markov Chainsp. 75
Construction of the Estimatorsp. 76
Asymptotic Properties of the Estimatorsp. 79
Example: a Textile Factory (Continuation)p. 95
Exercisesp. 98
Reliability Theory for Discrete-Time Semi-Markov Systemsp. 101
Reliability Function and Associated Measuresp. 102
Reliabilityp. 104
Availabilityp. 106
Maintainabilityp. 108
Failure Ratesp. 108
Mean Hitting Timesp. 111
Nonparametric Estimation of Reliabilityp. 115
Example: a Textile Factory (Continuation)p. 122
Exercisesp. 129
Hidden Semi-Markov Model and Estimationp. 131
Hidden Semi-Markov Modelp. 132
Estimation of a Hidden Semi-Markov Modelp. 136
Consistency of Maximum-Likelihood Estimatorp. 137
Asymptotic Normality of Maximum-Likelihood Estimatorp. 144
Monte Carlo Algorithmp. 150
EM Algorithm for a Hidden Semi-Markov Modelp. 151
Preliminariesp. 151
EM Algorithm for Hidden Model SM-M0p. 153
EM Algorithm for Hidden Model SM-M1p. 158
Proofsp. 163
Example: CpG Island Detectionp. 173
Exercisesp. 177
Lemmas for Semi-Markov Chainsp. 179
Lemmas for Hidden Semi-Markov Chainsp. 183
Lemmas for Hidden SM-M0 Chainsp. 183
Lemmas for Hidden SM-M1 Chainsp. 184
Some Proofsp. 185
Markov Chainsp. 195
Definition and Transition Functionp. 195
Strong Markov Propertyp. 197
Recurrent and Transient Statesp. 197
Stationary Distributionp. 199
Markov Chains and Reliabilityp. 200
Miscellaneousp. 203
Notationp. 207
Referencesp. 211
Indexp. 221
Table of Contents provided by Ingram. All Rights Reserved.

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