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
Probability Theory and Stochastic Processes
Introduction to probability theory
Probability density function
Discrete Hidden Markov Models
Hidden Markov model dynamics
Probability transitions estimation
Viterbi training algorithm
Architectures for Markov models
Continuous hidden Markov models
Probability density functions and Gaussian mixtures
Continuous hidden Markov model dynamics
Continuous observations Baum-Welch training algorithm
Autoregressive Markov models
Likelihood and probability density for AR models
Likelihood of an ARMM
ARMM parameters estimations
Time series prediction with ARMM
Solar radiation prediction
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.
"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