Издательство InTech, 2012, -124 pp.Over the last decade, a Bayesian network has become a popular representation for encoding uncertain expert knowledge in expert systems. A Bayesian network is a graphical model for probabilistic relationships among a set of variables. It is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data modeling. So what do Bayesian networks and Bayesian methods have to offer? There are at least four benefits described in the following.
One, Bayesian networks can readily handle incomplete data sets. For example, consider a classification or regression problem where two of the explanatory or input variables are strongly anti-correlated. This correlation is not a problem for standard supervised learning techniques, provided all inputs are measured in every case. When one of the inputs is not observed, however, many models will produce an inaccurate prediction, because they do not encode the correlation between the input variables. Bayesian networks offer a natural way to encode such dependencies.
Two, Bayesian networks allow one to learn about causal relationships. Learning about causal relationships is important for at least two reasons. The process is useful when we are trying to gain an understanding about a problem domain, for example, during exploratory data analysis. In addition, knowledge of causal relationships allows us to make predictions in the presence of interventions. For example, a marketing analyst may want to know whether or not it is worthwhile to increase exposure of a particular advertisement in order to increase the sales of a product. To answer this question, the analyst can determine whether or not the advertisement is a cause for increased sales, and to what degree. The use of Bayesian networks helps to answer such questions even when no experimental data about the effects of increased exposure is available.
Three, Bayesian networks, in conjunction with Bayesian statistical techniques, facilitate the combination of domain knowledge and data. Anyone who has performed a realworld modeling task knows the importance of prior or domain knowledge, especially when data is scarce or expensive to obtain. The fact that some commercial systems (i.e., expert systems) can be built from prior knowledge alone is a testament to the power of prior knowledge. Bayesian networks have a causal semantics that makes the encoding of causal prior knowledge particularly straightforward. In addition, Bayesian networks encode the strength of causal relationships with probabilities. Consequently, prior knowledge and data can be combined with well-studied techniques from Bayesian statistics.
Four, Bayesian methods in conjunction with Bayesian networks and other types of models offer an efficient and principled approach for avoiding the over fitting of data.
This book deals with the theory and algorithms for learning and probabilistic inference in Bayesian networks. This book also provides selected applications of Bayesian networks in several fields, including adaptive risk management, operational risk analysis, rangeland management and resiliency and interdependency of critical urban infrastructure. The book chapters are original manuscripts written by experienced researchers that have made significant contributions to the field of Bayesian networks. Although all chapters are self- contained, the reader should be familiar with texts using mathematical and statistical language to gain full benefit from the book. I am convinced that this book will be a very useful tool for anyone who is concerned with modelling systems containing causality with inherent uncertainty. I believe that readers will not only find the technical aspects for using and implementing Bayesian networks to solve their problem, but will also discover new approaches for how their current research and work can benefit from one of the major tools of the 21st century.
Making a Predictive Diagnostic Model for Rangeland Management by Implementing a State and Transition Model Within a Bayesian Belief Network (Case Study: Ghom-Iran).
Building a Bayesian Network Model Based on the Combination of Structure Learning Algorithms and Weighting Expert Opinions Scheme.
Using Dynamic Bayesian Networks for Investigating the Impacts of Extreme Events.
A Spatio-Temporal Bayesian Network for Adaptive Risk Management in Territorial Emergency Response Operations.
Probabilistic Inference for Hybrid Bayesian Networks.
BN Applications in Operational Risk Analysis: Scope, Limitations and Methodological Requirements.
