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. The 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. In order t.

The primary objective of the research is to develop and validate the approach and algorithms for efficiently learning a complex web of relations from a combination of prior knowledge, published literature, and high-dimensional data. Algorithms for inferring the structure of Bayesian networks (BNs) from data have become an increasingly popular method for uncovering the direct and indirect influences among variables in complex systems. A Bayesian model averaging method, Markov Chain Monte Carlo (MCMC), is typically applied for BN structural learning from data. However, existing state-of-the-art MCMC-based learning algorithms are rather slow in mixing and convergence in high-dimensional domains. To address these challenges, we first developed and tested intelligent strategies for prioritizing the structural search space using prior information. Second, we present a novel Markov blanket resampling (MBR) scheme that intermittently reconstructs the entire Markov blanket of nodes, thus allowing the sampler to more effectively traverse low-probability regions between local maxima. Experiments across a range of network sizes show that the MBR scheme outperforms other state-of-the-art algorithms, both in terms of learning performance and convergence rate. In particular, MBR achieves better learning performance when the number of observations is relatively small and faster convergence when the number of variables in the network is large. It is anticipated that our methodology will be especially useful for deciphering how genes and the environment interact to determine cancer risk by allowing BNs to be extended to a genome-wide scale.

Disk contains: Tool for building Bayesian networks -- Library of examples -- Library of proposed solutions to some exercises.

"This book focuses on methods widely used in modeling gene networks including structure discovery, learning, and optimization"--Provided by publisher.

This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.