Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research.
This book is suitable for advanced students and researchers with an applied background. This book discusses mixture and hidden Markov models for modeling behavioral data.
This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory.
Hidden Markov Models (HMMs) remains a vibrant area of research in statistics, with many new applications appearing since publication of the first edition.
In recent years, they have attracted growing interest in the area of computer vision as well. This book is a collection of articles on new developments in the theory of HMMs and their application in computer vision.
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.
Presents algorithms for using HMMs and explains the derivation of those algorithms for the dynamical systems community.
This handbook offers systemic applications of different methodologies that have been used for decision making solutions to the financial problems of global markets.
A basic knowledge of machine learning is preferred to get the best out of this guide.
Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.
(2010); Chen and Jiang (2011); Dong and Peng (2011); Peng and Dong (2011); Geramifard et al. (2011); Moghaddass and Zuo (2012a,b); Liu et al. (2012); Jiang et al. (2012); Su and Shen (2013); Boukra and Lebaroud (2014); Wang et al.