Hidden Markov Models with Applications in Computational Biology

Higher-order Hidden Markov Models (HMMs) serve as powerful extensions of their first-order counterparts, with applications in fields like speech recognition, image segmentation, and computational biology. This book offers a comprehensive introduction to the algorithmic foundations of higher-order HMMs, including novel extensions such as parsimonious models and those with scaled transition matrices. It also explores practical applications of these models in analyzing DNA microarray datasets, making it a valuable resource for readers familiar with first-order HMMs seeking deeper insights.