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Hidden Markov Models and Dynamical Systems

Hidden Markov Models and Dynamical Systems

  • Date Published: May 2009
  • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • format: Paperback
  • isbn: 9780898716658


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About the Authors
  • This text provides an introduction to hidden Markov models (HMMs) for the dynamical systems community. It is a valuable text for third or fourth year undergraduates studying engineering, mathematics, or science that includes work in probability, linear algebra and differential equations. The book presents algorithms for using HMMs, and it explains the derivation of those algorithms. It presents Kalman filtering as the extension to a continuous state space of a basic HMM algorithm. The book concludes with an application to biomedical signals. This text is distinctive for providing essential introductory material as well as presenting enough of the theory behind the basic algorithms so that the reader can use it as a guide to developing their own variants.

    • Features illustrations that use the Lorenz system, laser data, and natural language data
    • Supporting web site gives a working implementation of each algorithm from the book
    • Enables readers to develop their own variants
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    Product details

    • Date Published: May 2009
    • format: Paperback
    • isbn: 9780898716658
    • length: 143 pages
    • dimensions: 254 x 175 x 7 mm
    • weight: 0.27kg
    • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • Table of Contents

    1. Introduction
    2. Basic algorithms
    3. Variants and generalizations
    4. Continuous states and observations and Kalman filtering
    5. Performance bounds and a toy problem
    6. Obstructive sleep apnea
    Appendix A. Formulas for matrices and Gaussians
    Appendix B. Notes on software

  • Author

    Andrew M. Fraser, Los Alamos National Laboratory
    Andrew M. Fraser is a Technical Staff Member in the ISR division of the Los Alamos National Laboratory where he uses stochastic models in his work on signal analysis. He spent 15 years at Portland State University in Oregon serving on the faculties of both the Systems Science PhD Program and the Electrical and Computer Engineering Department before joining LANL in 2005. He earned a PhD in Physics from UT-Austin with a dissertation on the use of mutual information estimates in the analysis of chaotic time series. Before graduate school, he designed bipolar memory technology and products at Fairchild semiconductor. He is a member of SIAM and a Senior Member of the IEEE.

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