Probabilistic Reasoning and Bayesian Belief Networks

One of the most significant characteristics of an intelligent computer system is the ability to reason with judgmental knowledge. That is, how it uses heuristics, and improves its decision-making procedures in the light of examples which it is given. These heuristics are typically uncertain. Numerous methods have been suggested and are used for dealing with uncertainty. Many have been developed to overcome particular problems associated with the use of classical formalism for dealing with uncertainty, for example, probability theory. Recent work in theoretical statistics has demonstrated that it is possible to adopt a sound probabilistic approach to uncertain inference using Bayesian belief networks - a graphical representation of causal dependencies. This book summarizes some important work in the development of computational models of Bayesian belief networks, and their applications to medicine, transport and defence. The book should be of interest to all those working in: adaptive information processing, particularly in the allied fields of computer science, electrical engineering, physics and mathematics; also those researching in the neurosciences and branches of psychology and philsophy, particularly those concerned with neural modelling should benefit from this book. Corporate users should include IT specialists, production and control engineers, research and development departments, and consultants. There are two companion volumes to this book, "Neural Networks" and "Applications of Modern Heuristic Methods", which individually stand alone, but combined form a set treating a broad but integrated spectrum of techniques and tools for undertaking complex tasks.

Table of Contents:
From Bayesian netorks to causal networks; exact and approximate algorithms and their implementations in mixed graphical models; models and modelling in context; modelling ignorance in uncertainty theories; choosing network complexity; a system for hypothesis-driven data request; an efficient graphical algorithm for updating the estimates of the dispersal of gaseous waste after an accidental release; graphical representation of a network traffic model; a C++-class library for building Bayesian belief networks; smoothing noisy signals with Bayesian networks; efficient multiple-disorder diagnosis by strategic focusing; weighted inference rules and Bayesian belief networks; on the idiot vs proper Bayes approach in clinical diagnostic systems; constructing computationally-efficient Bayesian models via unsupervised clustering; Bayesian graphical models of the natural history of HIV-infection.