Develops the theory of Markov and semi-Markov processes in an elementary setting suitable for senior undergraduate and graduate students.
Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning.
The book provides a broad understanding of the models and their uses. After presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for HMMs.
Here is a work that adds much to the sum of our knowledge in a key area of science today.
This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory.
Here, working again with the reference probability technique, we discuss similar problems for “discrete-time” random fields, that is, sets of random variables taking their indices from unordered countable sets, such as, for example, ...
"This well-written book provides a clear and accessible treatment of the theory of discrete and continuous-time Markov chains, with an emphasis towards applications.
This book will be invaluable to researchers and postgraduate and senior undergraduate students in statistics.
This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care.
Topics and features: Introduces the formal framework for Markov models, describing hidden Markov models and Markov chain models, also known as n-gram models Covers the robust handling of probability quantities, which are omnipresent when ...
Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning ...