Hidden Markov Models For Time Series: An Introduction Using R

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]  eBooks & eLearning

Posted by ChrisRedfield at April 25, 2015
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.

Statistics: An Introduction Using R (Repost)  eBooks & eLearning

Posted by enmoys at June 11, 2016
Statistics: An Introduction Using R (Repost)

Statistics: An Introduction Using R By Michael J. Crawley
2005 | 342 Pages | ISBN: 0470022981 , 0470022973 | PDF | 3 MB
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…

Fourier Analysis of Time Series: An Introduction, 2nd edition (Repost)  eBooks & eLearning

Posted by nebulae at May 14, 2014
Fourier Analysis of Time Series: An Introduction, 2nd edition (Repost)

Peter Bloomfield, "Fourier Analysis of Time Series: An Introduction, 2nd edition"
English | 2000 | ISBN: 0471889482 | 288 pages | PDF | 3,2 MB

Statistics: An Introduction Using R (Repost)  eBooks & eLearning

Posted by enmoys at April 13, 2014
Statistics: An Introduction Using R (Repost)

Statistics: An Introduction Using R By Michael J. Crawley
2005 | 342 Pages | ISBN: 0470022981 , 0470022973 | PDF | 3 MB
Extracting Knowledge From Time Series: An Introduction to Nonlinear Empirical Modeling (Repost)

Boris P. Bezruchko, Dmitry A. Smirnov, "Extracting Knowledge From Time Series: An Introduction to Nonlinear Empirical Modeling"
English | 2010 | ISBN: 3642126006 | 420 pages | PDF | 14 MB

Fourier Analysis of Time Series: An Introduction, 2nd edition (repost)  eBooks & eLearning

Posted by arundhati at Aug. 4, 2013
Fourier Analysis of Time Series: An Introduction, 2nd edition (repost)

Peter Bloomfield, "Fourier Analysis of Time Series: An Introduction, 2nd edition"
2000 | ISBN: 0471889482 | 288 pages | PDF | 3,2 MB

The Analysis of Time Series: An Introduction, Fifth Edition  eBooks & eLearning

Posted by lenami at Oct. 24, 2010
The Analysis of Time Series: An Introduction, Fifth Edition

The Analysis of Time Series: An Introduction, Fifth Edition
Publisher: Chapman & Hall | ISBN: 0412716402 | edition 1996 | PDF | 304 pages | 12 mb

"As an introduction to techniques for analyzing discrete time series, this textbook explains probability models, the spectral density function, time-invariant linear systems, state-space models, nonlinear models, and multivariate time series models."-"Book News, Inc."
This tidy book is a highly readable, introductory survey to the topic of modern time series analysis. It excels in its ability to focus on the more intuitive aspects of analysis and model identification. The discussion of both time- and frequency-domain approaches is reasonably balanced, and Kalman filtering is also introduced.