Hidden Markov Models: Theory and Implementation using MATLAB (R)

This book provides a comprehensive and accessible introduction to Hidden Markov Models,suitable for researchers and practitioners in fields such as speech processing, computer vision, and natural language processing.
This book provides a comprehensive introduction to Hidden Markov Models, covering their analysis, synthesis, and translation into computer codes using MATLAB®. It is suitable for researchers and practitioners in fields such as speech processing, computer vision, and natural language processing.

Format: Paperback / softback
Length: 282 pages
Publication date: 31 March 2021
Publisher: Taylor & Francis Ltd

Target Audience: This book is designed for researchers,students,and practitioners in the fields of computer science,statistics,and applied mathematics who want to gain a deeper understanding of HMM.
This comprehensive book offers a unique and comprehensive approach to the analysis and synthesis of three different types of Hidden Markov Models (HMM). Unlike other books on the subject, it is generic and does not focus on a specific theme, such as speech processing. Instead, it presents a broad range of concepts related to HMM, from simple problems to advanced theory.

The book begins by introducing the basic principles of HMM and then proceeds to cover the analysis of both continuous and discrete Markov chains. It discusses the translation of HMM concepts from the realm of formal mathematics into computer code using MATLAB®, a powerful programming language for numerical computing.

One of the unique features 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 HMM 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.

Throughout the book, numerous examples are provided to supplement the mathematical notation and help readers understand the concepts more effectively. These examples cover a wide range of applications, including speech recognition, natural language processing, and machine learning.

The target audience for this book includes researchers, students, and practitioners in the fields of computer science, statistics, and applied mathematics who want to gain a deeper understanding of HMM. Whether you are a beginner or an experienced researcher, this book will provide you with the knowledge and tools you need to apply HMM in your own research and projects.

Weight: 522g
Dimension: 234 x 156 (mm)
ISBN-13: 9780367779344