A Comprehensive Guide to HSMM

A Comprehensive Guide to HSMM, 1st Edition, introduces the theoretical framework of Hidden Semi-Markov Models and maximum likelihood estimation methods. Intended for Master's students, PhD students, researchers, academic faculty, and practitioners in the modeling, analysis, or control of time series, this book details essential probabilistic structures for dynamic temporal processes.

The organizing logic of the text links core theoretical principles directly with practical implementation tools. It includes a comprehensive review of existing R software packages and Python software packages available for HSMM estimation, enabling quantitative analysts to connect estimation algorithms with software workflows.

The volume addresses advanced model extensions, focusing on multi-chain HSMM and controlled HSMM formulations alongside computational complexity and stochastic control. This mathematical breadth fits specialized graduate courses, providing researchers with systematic tools for analyzing multi-stream time series data.