Olivier Pourret & Patrick Naïm 
Bayesian Networks [PDF ebook] 
A Practical Guide to Applications

Bayesian Networks, the result of the convergence of artificial
intelligence with statistics, are growing in popularity. Their
versatility and modelling power is now employed across a variety of
fields for the purposes of analysis, simulation, prediction and
diagnosis.
This book provides a general introduction to Bayesian networks,
defining and illustrating the basic concepts with pedagogical
examples and twenty real-life case studies drawn from a range of
fields including medicine, computing, natural sciences and
engineering.
Designed to help analysts, engineers, scientists and
professionals taking part in complex decision processes to
successfully implement Bayesian networks, this book equips readers
with proven methods to generate, calibrate, evaluate and validate
Bayesian networks.
The book:
* Provides the tools to overcome common practical challenges such
as the treatment of missing input data, interaction with experts
and decision makers, determination of the optimal granularity and
size of the model.
* Highlights the strengths of Bayesian networks whilst also
presenting a discussion of their limitations.
* Compares Bayesian networks with other modelling techniques such
as neural networks, fuzzy logic and fault trees.
* Describes, for ease of comparison, the main features of the
major Bayesian network software packages: Netica, Hugin, Elvira and
Discoverer, from the point of view of the user.
* Offers a historical perspective on the subject and analyses
future directions for research.
Written by leading experts with practical experience of applying
Bayesian networks in finance, banking, medicine, robotics, civil
engineering, geology, geography, genetics, forensic science,
ecology, and industry, the book has much to offer both
practitioners and researchers involved in statistical analysis or
modelling in any of these fields.

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