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Bayesian Artificial Intelligence

12 April 2011

**Bayesian Artificial Intelligence**

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.**New to the Second Edition**...

12 April 2011

2011 | 491 | ISBN: 1439815917 | PDF | 3 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.

Bayesian Artificial Intelligence, 2nd Edition

12 April 2011

**Bayesian Artificial Intelligence, 2nd Edition**

Publisher: C.R.C Press | 2011 | 491 Pages | ISBN: 1439815917 | PDF | 3 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.

12 April 2011

Publisher: C.R.C Press | 2011 | 491 Pages | ISBN: 1439815917 | PDF | 3 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.

Data Analysis in Forensic Science: A Bayesian Decision Perspective

13 September 2010

**Data Analysis in Forensic Science: A Bayesian Decision Perspective**

Wiley | 2010 | ISBN: 0470998350 | 388 pages | PDF | 12 MB

This is the first text to examine the use of statistical methods in forensic science and bayesian statistics in combination.

The book is split into two parts: Part One concentrates on the philosophies of statistical inference. Chapter One examines the differences between the frequentist, the likelihood and the Bayesian perspectives, before Chapter Two explores the Bayesian decision–theoretic perspective further, and looks at the benefits it carries.

13 September 2010

Wiley | 2010 | ISBN: 0470998350 | 388 pages | PDF | 12 MB

This is the first text to examine the use of statistical methods in forensic science and bayesian statistics in combination.

The book is split into two parts: Part One concentrates on the philosophies of statistical inference. Chapter One examines the differences between the frequentist, the likelihood and the Bayesian perspectives, before Chapter Two explores the Bayesian decision–theoretic perspective further, and looks at the benefits it carries.

Bayesian Adaptive Methods for Clinical Trials

9 July 2013

**Bayesian Adaptive Methods for Clinical Trials**

2010 | ISBN: 1439825483 | PDF | 311 pages | 4 MB

Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer’s disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis.

9 July 2013

2010 | ISBN: 1439825483 | PDF | 311 pages | 4 MB

Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer’s disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis.

Bayesian Reliability (Springer Series in Statistics)

20 March 2011

**Bayesian Reliability (Springer Series in Statistics)**

Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based computational tools for implementing Bayesian methods....

20 March 2011

2008 | 437 | ISBN: 0387779485 | PDF | 4 Mb

Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based computational tools for implementing Bayesian methods....

Bayesian Networks in R with Applications in Systems Biology

9 November 2013

** Radhakrishnan Nagarajan, Marco Scutari, "Bayesian Networks in R: with Applications in Systems Biology" **

English | ISBN: 1461464455 | 2013 | 172 pages | PDF | 3 MB

Bayesian Networks in R with Applications in Systems Biology is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses on systems biology with emphasis on modeling pathways and signaling mechanisms from high-throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel paradigms using the approaches presented in the book.

9 November 2013

English | ISBN: 1461464455 | 2013 | 172 pages | PDF | 3 MB

Bayesian Networks in R with Applications in Systems Biology is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses on systems biology with emphasis on modeling pathways and signaling mechanisms from high-throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel paradigms using the approaches presented in the book.

Case Studies in Bayesian Statistical Modelling and Analysis

20 August 2013

**Case Studies in Bayesian Statistical Modelling and Analysis by Clair L. Alston, Kerrie L. Mengersen and Anthony N. Pettitt**

English | 2012 | ISBN: 1119941822 | 598 pages | PDF | 11,8 MB

Provides an accessible foundation to Bayesian analysis using real world models

This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance.

20 August 2013

English | 2012 | ISBN: 1119941822 | 598 pages | PDF | 11,8 MB

Provides an accessible foundation to Bayesian analysis using real world models

This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance.

Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks

3 January 2011**Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks** by Richard E. Neapolitan
MK | 2009 | ISBN: 0123704766 | 400 pages | PDF | 14 MB
The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics.

3 January 2011

Coherent Stress Testing: A Bayesian Approach to the Analysis of Financial Risk

22 December 2010**Coherent Stress Testing: A Bayesian Approach to the Analysis of Financial Risk** by Riccardo Rebonato
Wiley | 2010 | ISBN: 0470666013, 0470667362 | 238 pages | PDF | 12 MB
In Coherent Stress Testing: A Bayesian Approach, industry expert Riccardo Rebonato presents a groundbreaking new approach to this important but often undervalued part of the risk management toolkit.
Based on the author's extensive work, research and presentations in the area, the book fills a gap in quantitative risk management by introducing a new and very intuitively appealing approach to stress testing based on expert judgement and Bayesian networks.

22 December 2010

System and Bayesian Reliability: Essays in Honor of Professor Richard E. Barlow

4 October 2011

**System and Bayesian Reliability: Essays in Honor of Professor Richard E. Barlow**

This volume is a collection of articles on reliability systems and Bayesian reliability analysis. Written by reputable researchers, the articles are self-contained and are linked with literature reviews and new research ideas. The book is dedicated to Emeritus Professor Richard E. Barlow, who is well known for his pioneering research on reliability theory and Bayesian reliability analysis. ...

4 October 2011

2002 | 409 | ISBN: 9810248652 | PDF | 15 Mb

This volume is a collection of articles on reliability systems and Bayesian reliability analysis. Written by reputable researchers, the articles are self-contained and are linked with literature reviews and new research ideas. The book is dedicated to Emeritus Professor Richard E. Barlow, who is well known for his pioneering research on reliability theory and Bayesian reliability analysis. ...