Explore the foundational concepts of Hidden Markov Models (HMM) specifically tailored for time series data, presented in an accessible introduction. This Second Edition provides practical guidance on implementing HMMs using R, perfect for those looking to understand and apply these powerful statistical tools to sequential data analysis.
Explore the fundamentals of Hidden Markov Models (HMMs) and delve into the Baum-Welch algorithm, a powerful method for training HMMs. This guide provides a comprehensive overview of HMMs, explaining their underlying principles and applications, particularly in sequence modeling. We also cover the Baum-Welch algorithm as an Expectation-Maximization (EM) approach for learning the parameters of an HMM when the training data is incomplete or unobserved. Learn how these concepts can be applied in various fields, from speech recognition to bioinformatics.