logo
Product categories

EbookNice.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link.  https://ebooknice.com/page/post?id=faq


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookNice Team

(Ebook) Hidden Markov Models for Time Series: An Introduction Using R by Walter Zucchini, Iain L. MacDonald ISBN 9781584885733, 1584885734

  • SKU: EBN-1144254
Zoomable Image
$ 32 $ 40 (-20%)

Status:

Available

4.7

31 reviews
Instant download (eBook) Hidden Markov Models for Time Series: An Introduction Using R after payment.
Authors:Walter Zucchini, Iain L. MacDonald
Pages:278 pages.
Year:2009
Editon:1st
Publisher:Chapman and Hall/CRC
Language:english
File Size:2.06 MB
Format:pdf
ISBNS:9781584885733, 1584885734
Categories: Ebooks

Product desciption

(Ebook) Hidden Markov Models for Time Series: An Introduction Using R by Walter Zucchini, Iain L. MacDonald ISBN 9781584885733, 1584885734

Reveals How HMMs Can Be Used as General-Purpose Time Series ModelsImplements all methods in R Hidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide range of time series types, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out computations for parameter estimation, model selection and checking, decoding, and forecasting.Illustrates the methodology in action After presenting the simple Poisson HMM, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference. Through examples and applications, the authors describe how to extend and generalize the basic model so it can be applied in a rich variety of situations. They also provide R code for some of the examples, enabling the use of the codes in similar applications.Effectively interpret data using HMMs This book illustrates the wonderful flexibility of HMMs as general-purpose models for time series data. It provides a broad understanding of the models and their uses.
*Free conversion of into popular formats such as PDF, DOCX, DOC, AZW, EPUB, and MOBI after payment.

Related Products