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 **tarantoga** at Feb. 15, 2017

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

Posted by **arundhati** at Dec. 16, 2016

2014 | ISBN: 0470979739 | English | 472 pages | PDF | 3 MB

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

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

Posted by **roxul** at May 3, 2016

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

Posted by **exLib** at Jan. 1, 2016

ITAvE | 2015 | ISBN: 9535105566 9789535105565 | 123 pages | PDF | 11 MB

Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various degrees of uncertainty in a mathematically sound and computationally efficient way. A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest.

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 **AlenMiler** at Aug. 25, 2015

English | 15 Dec. 2010 | ISBN: 364205885X, 3540208763 | 344 Pages | PDF | 12.75 MB

In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics.