This text is a reprint of the seminal 1989 book Probabilistic
Reasoning in Expert systems: Theory and Algorithms, which helped
serve to create the field we now call Bayesian networks. It
introduces the properties of Bayesian networks (called causal
networks in the text), discusses algorithms for doing inference in
Bayesian networks, covers abductive inference, and provides an
introduction to decision analysis. Furthermore, it compares
rule-base experts systems to ones based on Bayesian networks, and
it introduces the frequentist and Bayesian approaches to
probability. Finally, it provides a critique of the maximum entropy
formalism. Probabilistic Reasoning in Expert Systems was written
from the perspective of a mathematician with the emphasis being on
the development of theorems and algorithms. Every effort was made
to make the material accessible. There are ample examples
throughout the text. This text is important reading for anyone
interested in both the fundamentals of Bayesian networks and in the
history of how they came to be. It also provides an insightful
comparison of the two most prominent approaches to probability.
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