Posted by **nebulae** at April 30, 2017

English | ISBN: 1119125456 | 2016 | 640 pages | PDF | 19 MB

Posted by **Underaglassmoon** at Aug. 1, 2016

Springer | Statistics | August 28, 2016 | ISBN-10: 3319327887 | 327 pages | pdf | 3.14 mb

Authors: Phadia, Eswar G.

Provides valuable resource for nonparametric Bayesian analysis of big data

Includes a section on machine learning

Shows practical examples

Posted by **Underaglassmoon** at July 19, 2016

Springer | Signals & Communication | August 16, 2016 | ISBN-10: 9811003785 | 114 pages | pdf | 6.6 mb

Authors: Davey, S., Gordon, N., Holland, I., Rutten, M., Williams, J.

Written by members of the MH370 search team

Contains a tutorial description of available data allowing readers to work out their own solutions

Posted by **arundhati** at May 19, 2016

Posted by **Specialselection** at Nov. 8, 2012

English | 2006-11-10 | ISBN: 1584885440 | 350 pages | PDF | 3.7 mb

Posted by **interes** at Sept. 22, 2012

English | ISBN: 0470120959 | 2007 | PDF | 951 pages | 235 MB

The first comprehensive development of Bayesian Bounds for parameter estimation and nonlinear filtering/tracking

Bayesian estimation plays a central role in many signal processing problems encountered in radar, sonar, communications, seismology, and medical diagnosis. There are often highly nonlinear problems for which analytic evaluation of the exact performance is intractable. A widely used technique is to find bounds on the performance of any estimator and compare the performance of various estimators to these bounds.

Posted by **Stalker1984** at Oct. 9, 2011

C,R,C Pr..ess | ISBN: 1439815917 | 2010 | PDF | 479 pages | 3,31 mb

Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.

Posted by **tarantoga** at Sept. 2, 2011

Publisher: S.r.n.e. | ISBN: 1849961867 | October 28, 2011 | PDF | 240 pages | 4 MB

Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing inference on these problems. Inference in the book employs a modern computational approach known as Markov chain Monte Carlo (MCMC). The MCMC approach may be implemented using custom-written routines or existing general purpose commercial or open-source software. This book uses an open-source program called OpenBUGS (commonly referred to as WinBUGS) to solve the inference problems that are described.

Posted by **lout** at April 11, 2011

Publisher: C.R.C Press; 2 edition 2011 | 491 Pages | ISBN: 1439815917 | PDF | 3 MB

Posted by **tot167** at Nov. 9, 2010

Sciyo | 2010 | ISBN-13: 9789533071244 | 432 pages | PDF | 17,7 MB