Probabilistic graphical models and decision graphs are powerful
modeling tools for reasoning and decision making under uncertainty.
As modeling languages they allow a natural specification of problem
domains with inherent uncertainty, and from a computational
perspective they support efficient algorithms for automatic
construction and query answering. This includes belief updating,
finding the most probable explanation for the observed evidence,
detecting conflicts in the evidence entered into the network,
determining optimal strategies, analyzing for relevance, and
performing sensitivity analysis.
The book introduces probabilistic graphical models and decision
graphs, including Bayesian networks and influence diagrams. The
reader is introduced to the two types of frameworks through
examples and exercises, which also instruct the reader on how to
build these models.
The book is a new edition of Bayesian Networks and Decision
Graphs by Finn V. Jensen. The new edition is structured into two
parts. The first part focuses on probabilistic graphical models.
Compared with the previous book, the new edition also includes a
thorough description of recent extensions to the Bayesian network
modeling language, advances in exact and approximate belief
updating algorithms, and methods for learning both the structure
and the parameters of a Bayesian network. The second part deals
with decision graphs, and in addition to the frameworks described
in the previous edition, it also introduces Markov decision
processes and partially ordered decision problems. The authors
also
- provide a well-founded practical introduction to Bayesian
networks, object-oriented Bayesian networks, decision trees,
influence diagrams (and variants hereof), and Markov decision
processes.
- give practical advice on the construction of Bayesian networks,
decision trees, and influence diagrams from domain knowledge.
- give several examples and exercises exploiting computer systems
for dealing with Bayesian networks and decision graphs.
- present a thorough introduction to state-of-the-art solution
and analysis algorithms.
The book is intended as a textbook, but it can also be used for
self-study and as a reference book.
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