Posted by **rainbowl76** at Jan. 13, 2009

Prentice Hall | 2003-04-06 | ISBN: 0130125342 | 678pages | PDF | 3.5MB

Posted by **ChrisRedfield** at July 19, 2015

Published: 2008-01-15 | ISBN: 1586038214 | PDF | 148 pages | 1.27 MB

Posted by **libr** at Aug. 24, 2012

English | 2008-01-15 | ISBN: 1586038214 | PDF | 148 pages | 1.27 MB

This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. Topics discussed are; basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and the concept of incomplete data.

Posted by **insetes** at Oct. 13, 2017

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

Posted by **readerXXI** at Oct. 6, 2017

English | 2015 | ISBN: 178398760X | 168 Pages | Mobi+Code Files | 6.5 MB

Posted by **roxul** at May 21, 2017

English | 2013 | ISBN-10: 1461451035 | 412 pages | PDF | 3 MB

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 **naag** at March 25, 2017

English | ISBN: 3319283782 | 2016 | 284 pages | PDF | 15 MB

This volume constitutes the refereed proceedings of the Second International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015, held in Yokohama, Japan, in November 2015.

Posted by **tarantoga** at Feb. 15, 2017

ISBN: 1461464455 | 2013 | EPUB | 157 pages | 905 KB

Posted by **ChrisRedfield** at Jan. 25, 2017

Published: 2012-11-29 | ISBN: 1461451035, 1493900293 | PDF | 382 pages | 3.04 MB