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  • Format: PDF

Produktdetails
  • Verlag: Springer New York
  • Erscheinungstermin: 7. Januar 2009
  • Englisch
  • ISBN-13: 9780387731735
  • Artikelnr.: 37286578
Autorenporträt
Two important types of models for reliability and genetics, the semi-Markov and hidden semi-Markov models are presented in this book. Their importance relies on the fact that they generalize several previous models and provide new possibilities to handle real problems. For reliability scientists and engineers, this book comes with a new method for studying systems reliability and it offers modelling and estimation tools. As for the biologists, this work offers them a more adapted model for DNA analysis, namely the hidden semi-Markov model, which is more flexible than the hidden Markov models, extensively used in this field. Moreover, the hidden semi-Markov framework can be used in many other applications, such as reliability, signal treatment, speech or image processing, among others.
Rezensionen
From the reviews:

"Overall, this is a stimulating book. As it says, the discrete-time framework has been underused in the past in the view of the fact that much real data in practice (I would even say most) is recorded in discrete time. Also, the applications listed, particularly the DNA sequencing, are important ad timely." (Martin Crowder, International Statistical Review 2009, 77, 2)

"Barbu and Limnios's goal is to present a complete picture of the basic theory of finite state space semi-Markov processes in discrete time, describe its applications to reliability and DNA analysis, and obtain estimation results for hidden semi-Markov models (HSSM). ... Each chapter contains several exercises. There are five appendices that render the book self-contained. ... I highly recommend this book for applied probabilists and statisticians interested in reliability and DNA analysis, and for theoretically oriented reliability and bioinformatics engineers." (P. R. Parthasarathy, ACM Computing Reviews, March, 2009)

"...this book will be useful as a reference for researchers interested in discrete-time semi-Markov and hidden semi-Markov processes..." (Biometrics 65, 1312, December 2009)