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.
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.
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.
Inhaltsangabe
1. Preface 2. Chapter 1: Introduction 3. Chapter 2: Basic Algorithms: Chapter 3: Variants and Generalizations 4. Chapter 4: Continuous States and Observations and Kalman Filtering 5. Chapter 5: Performance Bounds and a Toy Problem 6. Chapter 6: Obstructive Sleep Apnea 7. Appendix A: Formulas for Matrices and Gaussians 8. Appendix B: Notes on Software 9. Bibliography 10. Index
1. Preface 2. Chapter 1: Introduction 3. Chapter 2: Basic Algorithms: Chapter 3: Variants and Generalizations 4. Chapter 4: Continuous States and Observations and Kalman Filtering 5. Chapter 5: Performance Bounds and a Toy Problem 6. Chapter 6: Obstructive Sleep Apnea 7. Appendix A: Formulas for Matrices and Gaussians 8. Appendix B: Notes on Software 9. Bibliography 10. Index
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