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#### Strategic Economic Decision-Making

Springer New York 2012; US$ 41.80Strategic Economic Decision-Making: Using Bayesian Belief Networks to Solve Complex Problems is a quick primer on the topic that introduces readers to the basic complexities and nuances associated with learning Bayes’ theory and inverse probability for the first time. This brief is meant for non-statisticians who are unfamiliar with Bayes’ theorem, walking them... more...

#### Die Theorie, die nicht sterben wollte

Springer Berlin Heidelberg 2013; US$ 23.08Die Autorin macht die Bedeutung des Bayes-Theorems einem breiten Publikum verständlich: von seiner Entdeckung im 18. Jahrhundert, der Weiterentwicklung durch Laplace über die vielen Kontroversen, die es ausgelöst hat, bis zur allgegenwärtigen Anwendung heute. more...

#### Risk Assessment and Decision Analysis with Bayesian Networks

CRC Press 2012; US$ 89.95Although many Bayesian Network (BN) applications are now in everyday use, BNs have not yet achieved mainstream penetration. Focusing on practical real-world problem solving and model building, as opposed to algorithms and theory, Risk Assessment and Decision Analysis with Bayesian Networks explains how to incorporate knowledge with data to develop... more...

#### Bayesian Methods

CRC Press 2014; US$ 83.00An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social Scientists Now that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details... more...

#### Bayesian Networks

CRC Press 2014; US$ 89.95Understand the Foundations of Bayesian Networks—Core Properties and Definitions Explained Bayesian Networks: With Examples in R introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples in R illustrate each step of the modeling process. The examples start from the simplest notions and gradually increase in complexity.... more...

#### Bayesian Missing Data Problems

CRC Press 2009; US$ 109.95Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors. The methods are based on the inverse Bayes formulae discovered by one of the author in 1995. Applying the Bayesian approach to important real-world... more...

#### Bayesian Artificial Intelligence, Second Edition

CRC Press 2010; US$ 114.95Updated 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... more...

#### Bayesian Nonparametric Data Analysis

Springer International Publishing 2015; US$ 89.58This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric... more...

#### Bayesian Theory

Wiley 2009; US$ 509.00 US$ 458.10This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion... more...

#### Bayes Linear Statistics

Wiley 2007; US$ 185.00 US$ 166.50Bayesian methods combine information available from data with any prior information available from expert knowledge. The Bayes linear approach follows this path, offering a quantitative structure for expressing beliefs, and systematic methods for adjusting these beliefs, given observational data. The methodology differs from the full Bayesian methodology... more...