Hidden Markov Models : Theory and Implementation using MATLAB® book cover
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Hidden Markov Models
Theory and Implementation using MATLAB®




ISBN 9780367203498
Published August 13, 2019 by CRC Press
296 Pages

 
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Book Description

This book presents, in an integrated form, both the analysis and synthesis of three different types of hidden Markov models. Unlike other books on the subject, it is generic and does not focus on a specific theme, e.g. speech processing. Moreover, it presents the translation of hidden Markov models’ concepts from the domain of formal mathematics into computer codes using MATLAB®. The unique feature of this book is that the theoretical concepts are first presented using an intuition-based approach followed by the description of the fundamental algorithms behind hidden Markov models using MATLAB®. This approach, by means of analysis followed by synthesis, is suitable for those who want to study the subject using a more empirical approach.

Key Selling Points:

  • Presents a broad range of concepts related to Hidden Markov Models (HMM), from simple problems to advanced theory
  • Covers the analysis of both continuous and discrete Markov chains
  • Discusses the translation of HMM concepts from the realm of formal mathematics into computer code
  • Offers many examples to supplement mathematical notation when explaining new concepts

Table of Contents

Introduction

System models

Markov chains

Book outline

 

Probability Theory and Stochastic Processes

Introduction

Introduction to probability theory

Probability density function

Statistical moments

Summary

 

Discrete Hidden Markov Models

Introduction

Hidden Markov model dynamics

Probability transitions estimation

Viterbi training algorithm

Gradient-based algorithms

Architectures for Markov models

Summary

 

Continuous hidden Markov models

Introduction

Probability density functions and Gaussian mixtures

Continuous hidden Markov model dynamics

Continuous observations Baum-Welch training algorithm

Summary

 

Autoregressive Markov models

Introduction

ARMM structure

Likelihood and probability density for AR models

Likelihood of an ARMM

ARMM parameters estimations

Time series prediction with ARMM

Summary

 

Selected Applications

Cardiotocography classification

Solar radiation prediction

Summary

 

Glossary

 

References

Index

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Author(s)

Biography

João Paulo Coelho is an adjunct professor, and currently the Electrical Engineering course director, at the Polytechnic Institute of Bragança. He is also a researcher at CeDRI and holds a Ph.D. degree in computational intelligence applied to agricultural greenhouses. He has been involved, as a researcher member, in several scientific projects at both the national and European level. His research interests include control systems design, machine learning, electronic instrumentation, embedded systems and discrete-event computer simulation.

 

Tatiana M. Pinho graduated in Energy Engineering from the University of Trás-os-Montes e Alto Douro (UTAD), Portugal in 2011 and received the MSc degree in Energy Engineering from UTAD in 2013. In 2018, she received the Ph.D. degree in Electrical and Computer Engineering in UTAD and INESC TEC Technology and Science, supported by the FCT. Presently she is a postdoctoral researcher at the INESC TEC and her research interests include systems’ modeling and adaptive control.

José Boaventura-Cunha graduated in Electronics and Telecommunications Engineering and has a Ph.D. degree in Electrical and Computer Engineering. Presently he is an Associate Professor with habilitation at the UTAD University, a senior researcher at the INESC-TEC and member of IFAC and IEEE. He has coordinated/participated in several national and international research projects aiming the development of new instrumentation, modelling and control technologies applied to agriculture. His research interests include modeling, system identification and adaptive control.

Reviews

"A distinguishing feature of this book is that it provides the MATLAB code for the various algorithms covered. This would make it an excellent text for a course in the subject, as it would enable the students to experiment themselves with the algorithms encountered. Another good feature is that each chapter ends with a clear summary. All libraries serving programs in computer science should acquire this volume, and it would be worth considering as a textbook by instructors teaching courses on hidden Markov models."

— R. Bharath, emeritus, Northern Michigan University in CHOICE magazine