Posted by **readerXXI** at April 10, 2017

English | 2015 | ISBN: 178398760X | 165 Pages | True PDF | 1.48 MB

Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems.

Posted by **nebulae** at Dec. 14, 2016

English | ISBN: 1574446134 | 2006 | 304 pages | PDF | 4 MB

Posted by **enmoys** at May 26, 2016

2015 | 168 Pages | ISBN: 178398760X | EPUB | 3 MB

Posted by **Underaglassmoon** at May 24, 2016

Springer | Statistical Theory and Methods | June 21, 2016 | ISBN-10: 9811008884 | 529 pages | pdf | 3.97 mb

Authors: Dixit, Ulhas Jayram

Presents sophisticated mathematical proofs in a simple and easy-to-follow language

Discusses fundamental topics common to many fields of statistical inference, and which offer a point of departure for in-depth study

Posted by **DZ123** at Nov. 2, 2015

English | 1992 | ISBN: 0262050463 | DJVU | pages: 287 | 1,9 mb

Posted by **AlenMiler** at Oct. 30, 2015

English | 28 Oct. 2015 | ISBN: 178398760X | 168 Pages | AZW3 (Kindle)/HTML/EPUB/PDF (conv) | 19 MB

This book is for statisticians, analysts, and data scientists who want to build a Bayes-based system with R and implement it in their day-to-day models and projects. It is mainly intended for Data Scientists and Software Engineers who are involved in the development of Advanced Analytics applications.

Posted by **arundhati** at Jan. 13, 2015

2008 | ISBN-10: 0471696935 | 664 pages | PDF | 26 MB

Posted by **AlenMiler** at Aug. 19, 2014

November 25, 2013 | ISBN: 3642378862 | Pages: 376 | PDF | 8 MB

This book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. Two introductory chapters discuss the importance of statistical models in applied quantitative research and the central role of the likelihood function. The rest of the book is divided into three parts. The first describes likelihood-based inference from a frequentist viewpoint. Properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic are discussed in detail. In the second part, likelihood is combined with prior information to perform Bayesian inference. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods. Modern numerical techniques for Bayesian inference are described in a separate chapter. Finally two more advanced topics, model choice and prediction, are discussed both from a frequentist and a Bayesian perspective.

A comprehensive appendix covers the necessary prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis.

Posted by **Specialselection** at Feb. 15, 2014

English | 2010-09-20 | ISBN: 0521192498 | 276 pages | PDF | 3.6 mb

Posted by **ParRus** at July 24, 2017

WEBRip | English | MP4 + Project files | 1920 x 1080 | AVC ~229 kbps | 30 fps

AAC | 192 Kbps | 48.0 KHz | 2 channels | ~40.5 hours | 6.22 GB

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.