Hidden Markov Models For Time Series

Hidden Markov Models for Time Series: An Introduction Using R, Second Edition  eBooks & eLearning

Posted by arundhati at Dec. 9, 2016
Hidden Markov Models for Time Series: An Introduction Using R, Second Edition

Walter Zucchini, Iain L. MacDonald, "Hidden Markov Models for Time Series: An Introduction Using R, Second Edition"
2016 | ISBN-10: 1482253836 | 398 pages | PDF | 46 MB
Hidden Markov Models for Time Series: An Introduction Using R [Repost]

Walter Zucchini, Iain L. MacDonald - Hidden Markov Models for Time Series: An Introduction Using R
Published: 2009-04-28 | ISBN: 1584885734 | PDF | 269 pages | 2 MB
Hidden Markov Models for Time Series: An Introduction Using R (Monographs on Statistics and Applied Probability)

Hidden Markov Models for Time Series
Walter Zucchini, Iain L. MacDonald | Chapman & Hall/CRC | 2009-04-28 | ISBN:1584885734 | Pages: 269 | PDF | 5.3MB

This book illustrates the wonderful flexibility of HMMs as general-purpose models for time series data. It presents an accessible overview of HMMs for analyzing time series data, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts, and categorical observations. It explores a variety of applications in animal behavior, finance, epidemiology, climatology, and sociology. The authors discuss how to employ the freely available computing environment R to carry out computations for parameter estimation, model selection and checking, decoding, and forecasting. They provide all of the data sets analyzed in the text online.

Hidden Markov Models for Bioinformatics (Computational Biology) by T. Koski [Repost]

Hidden Markov Models for Bioinformatics (Computational Biology) by T. Koski
English | Nov 30, 2001 | ISBN: 1402001363 | 404 Pages | DJVU | 2 MB

The purpose of this book is to give a thorough and systematic introduction to probabilistic modeling in bioinformatics. The book contains a mathematically strict and extensive presentation of the kind of probabilistic models…

Latent Markov Models for Longitudinal Data  

Posted by nebulae at Dec. 25, 2015
Latent Markov Models for Longitudinal Data

Francesco Bartolucci, Alessio Farcomeni, Fulvia Pennoni, "Latent Markov Models for Longitudinal Data"
English | ISBN: 1439817081 | 2012 | 252 pages | PDF | 2 MB
Application of Hidden Markov Models in Speech Recognition

Mark Gales, Steve Young, "Application of Hidden Markov Models in Speech Recognition"
English | 2008 | ISBN: 1601981201 | PDF | pages: 124 | 1,6 mb

Grammar-Based Feature Generation for Time-Series Prediction  

Posted by Underaglassmoon at March 21, 2015
Grammar-Based Feature Generation for Time-Series Prediction

Grammar-Based Feature Generation for Time-Series Prediction
Springer | Applied Mathematics | Feb. 17 2015 | ISBN-10: 9812874100 | 99 pages | pdf | 4.4 mb

by Anthony Mihirana De Silva (Author), Philip H. W. Leong (Author)
This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation.

Hidden Markov Models: Estimation and Control  

Posted by step778 at Feb. 26, 2015
Hidden Markov Models: Estimation and Control

Robert J Elliott, Lakhdar Aggoun, John B. Moore, "Hidden Markov Models: Estimation and Control"
2008 | pages: 374 | ISBN: 0387943641 | PDF | 5,4 mb
Handbook of Hidden Markov Models in Bioinformatics (Repost)

Handbook of Hidden Markov Models in Bioinformatics (Chapman & Hall/CRC Mathematical and Computational Biology) by Martin Gollery
English | 2008 | ISBN: 1584886846 | 156 Pages | PDF | 9 MB

Demonstrating that many useful resources, such as databases, can benefit most bioinformatics projects, the Handbook of Hidden Markov Models in Bioinformatics focuses on how to choose and use various methods and programs available for hidden Markov models (HMMs).
Hidden Markov Models in Finance: Further Developments and Applications, Volume II (repost)

Hidden Markov Models in Finance: Further Developments and Applications, Volume II by Rogemar S. Mamon and Robert J. Elliott
English | 2014 | ISBN: 1489974415 | 261 pages | PDF | 4,6 MB

Since the groundbreaking research of Harry Markowitz into the application of operations research to the optimization of investment portfolios, finance has been one of the most important areas of application of operations research. The use of hidden Markov models (HMMs) has become one of the hottest areas of research for such applications to finance.