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, and when communicated to a computer, they can easily be compiled. Furthermore, handy algorithms are developed for analyses of the models and for providing responses to a wide range of requests like belief updating, determining optimal strategies, conflict analyses of evidence, most probable explanation, etc. The book emphasizes both the human and the computer side.

Part I gives a thorough introduction to Bayesian networks as well as decision trees and infulence diagrams, and through examples and exercises, the reader is instructed in building graphical models from domain knowledge. This part is self-contained and it does not require other background than standard secondary school mathematics.

Part II is devoted to the presentation of algorithms and complexity issues. Theis part is also self-contained, but it requires that the reader is familiar with working with texts in the mathematical language.

The author also:

• Provides a well-founded practical introduction to Bayesian networks, decision trees and influence diagrams
• Gives several examples and exercises exploiting the computer systems for Bayesian netowrks and influence diagrams
• Gives practical advice on constructiong Bayesian networks and influence diagrams from domain knowledge.
• Embeds decision making into the framework of Bayesian networks
• Presents in detail the currently most efficient algorithms for probability updating in Bayesian networks
• Discusses a wide range of analyes tools and model requests together with algorithms for calculation of responses.

Contents

• Preface

I A Practical Guide to Normative Systems 1

• 1 Causal and Bayesian Networks 3
• 2 Building Models 35
• 3 Learning, Adaptation, and Tuning 79
• 4 Decision Graphs 109

II Algorithms for Normative Systems 157

• 5 Belief Updating in Bayesian Networks 159
• 6 Bayesian Network Analysis Tools 201
• 7 Algorithms for Influence Diagrams 225
• List of Notation 253
• Bibliography 255
• Index 263