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Viser: Hidden Markov Models and Dynamical Systems
Hidden Markov Models and Dynamical Systems
Andrew M. . Fraser
(2008)
Sprog: Engelsk
Society for Industrial and Applied Mathematics
1.292,00 kr.
Denne bog er endnu ikke udgivet. Den forventes Mar 2008.
Detaljer om varen
- Paperback: 144 sider
- Udgiver: Society for Industrial and Applied Mathematics (Marts 2008)
- ISBN: 9780898716658
Hidden Markov models (HMMs) are discrete-state, discrete-time, stochastic dynamical systems. They are often used to approximate systems with continuous state spaces operating in continuous time. In addition to introducing the basic ideas of HMMs and algorithms for using them, this book explains the derivations of the algorithms with enough supporting theory to enable readers to develop their own variants. The book also presents Kalman filtering as an extension of ideas from basic HMMs to models with continuous state spaces.
Although applications of HMMs have become numerous (396,000 Google hits) since they emerged as the key technology for speech recognition in the 1980s, no introductory book on HMMs in general is available. This text aims to fill that gap.
Hidden Markov Models and Dynamical Systems features illustrations that use the Lorenz system, laser data, and natural language data. The concluding chapter presents the application of HMMs to detecting sleep apnea in experimentally measured electrocardiograms. Algorithms are given in pseudocode in the text, and a working implementation of each algorithm is available on the accompanying website.
Although applications of HMMs have become numerous (396,000 Google hits) since they emerged as the key technology for speech recognition in the 1980s, no introductory book on HMMs in general is available. This text aims to fill that gap.
Hidden Markov Models and Dynamical Systems features illustrations that use the Lorenz system, laser data, and natural language data. The concluding chapter presents the application of HMMs to detecting sleep apnea in experimentally measured electrocardiograms. Algorithms are given in pseudocode in the text, and a working implementation of each algorithm is available on the accompanying website.
Preface
Chapter 1: Introduction
Chapter 2: Basic Algorithms:
Chapter 3: Variants and Generalizations
Chapter 4: Continuous States and Observations and Kalman Filtering
Chapter 5: Performance Bounds and a Toy Problem
Chapter 6: Obstructive Sleep Apnea Appendix A: Formulas for Matrices and Gaussians Appendix B: Notes on Software Bibliography Index
Chapter 1: Introduction
Chapter 2: Basic Algorithms:
Chapter 3: Variants and Generalizations
Chapter 4: Continuous States and Observations and Kalman Filtering
Chapter 5: Performance Bounds and a Toy Problem
Chapter 6: Obstructive Sleep Apnea Appendix A: Formulas for Matrices and Gaussians Appendix B: Notes on Software Bibliography Index
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