Structural Equation Modelling A Bayesian Approach

Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have...

 Bayesian Approaches to Clinical Trials and Health-Care Evaluation

The Bayesian approach involves collecting data from past experience in order to reach conclusions about future events. Bayesian methods have become increasingly popular in recent years, notably in medical research. There are a large number of books on Bay...

 Bayesian nets and causality Philosophical and computational foundations

Bayesian nets are widely used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. This book, aimed at researchers and graduate s...

 Bayesian Nonparametrics Via Neural Networks

Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in...

 Learning Bayesian Networks

For courses in Bayesian Networks or Advanced Networking focusing on Bayesian networks found in departments of Computer Science, Computer Engineering and Electrical Engineering. Also appropriate as a supplementary text in courses on Expert Systems, Machine...

 Bayesian Networks and Decision Graphs

Bayesian networks and decision graphs are formal graphical languages for representation and communication of decision scenarios requiring reasoning under uncertainty. Their strengths are two-sided. It is easy for humans to construct and to understand them...