隠れマルコフ・モデルと応用 Hidden Markov Models and Applications

著者名：Bouguila, Nizar (EDT)/Fan, Wentao (EDT)/Amayri, Manar (EDT)

Springer（2022/05/19発売）

• 言語：ENG
• ISBN：9783030991418
• eISBN：9783030991425

Description

This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications.

Table of Contents

Chapter1. A Roadmap to Hidden Markov Models and A Review of its Application in Occupancy Estimation.- Chapter2. Bounded asymmetric Gaussian mixture-based hidden Markov models.- Chapter3. Using HMM to model neural dynamics and decode useful signals for neuroprosthetic control.- Chapter4. Fire Detection in Images with Discrete Hidden Markov Models.- Chapter5. Hidden Markov Models: Discrete Feature Selection in Activity Recognition.- Chapter6. Bayesian Inference of Hidden Markov Models using Dirichlet Mixtures.- Chapter7. Online learning of Inverted Beta-Liouville HMMs for Anomaly Detection in Crowd Scenes.- Chapter8. A Novel Continuous Hidden Markov Model for Modeling Positive Sequential Data.- Chapter9. Multivariate Beta-based Hidden Markov Models Applied to Human Activity Recognition.- Chapter10. Multivariate Beta-based Hierarchical Dirichlet Process Hidden Markov Models in Medical Applications.- Chapter11. Shifted-Scaled Dirichlet Based Hierarchical Dirichlet Process Hidden Markov Models with Variational Inference Learning.