Hidden Markov Models : Theory and Implementation using MATLAB®

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Hidden Markov Models : Theory and Implementation using MATLAB®

  • 著者名:Coelho, João Paulo/Pinho, Tatiana M./Boaventura-Cunha, José
  • 価格 ¥10,912 (本体¥9,920)
  • CRC Press(2019/08/02発売)
  • 夏休みの締めくくり!Kinoppy 電子書籍・電子洋書 全点ポイント30倍キャンペーン(~8/24)
  • ポイント 2,970pt (実際に付与されるポイントはご注文内容確認画面でご確認下さい)
  • 言語:ENG
  • ISBN:9780367779344
  • eISBN:9780429536632

ファイル: /

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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