Programming Bayesian Network Solutions with Netica, by Owen Woodberry and Steven Mascaro.
Provides an introduction to programming Bayesian Networks in Java with Netica. Assumes minimal programming experience and a basic understanding of BNs.
Ordering Information

Bayesian Artificial Intelligence 		Bayesian Artificial Intelligence, by Kevin B. Korb and Ann Nicholson.
From the Publisher's website: "Bayesian Artificial Intelligence presents elements of Bayesian network technology, automated causal discovery, and learning probabilities from data along with extensive motivational examples of using these technologies to develop probabilistic expert systems. This practical, very accessible introduction balances the causal discovery of networks with the Bayesian inference procedures that use a network once it is found. The authors emphasize understanding and intuition, so they keep the mathematical details to a minimum, but also provide the algorithms and technical background needed for applications. They illustrate at length a number of applications and discuss application software in detail."
Second edition available from Amazon.
The December 2006 issue of The Canadian Journal of Forest Research focuses on the use of Bayesian Belief Networks in Natural Resource Management, including 6 papers featuring Netica. Contents of Volume 36, Number 12 can be viewed from the NRC Canada website.
Bayesian Networks: A Practical Guide to Applications, edited by Olivier Pourret, Patrick Naim and Bruce Marcot.
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 title equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks.
Planning Improvements in Natural Resources Management, by Jeremy Cain.
This book offers numerous guidelines for using Bayes nets to manage natural resource development projects, using Netica as its example software. It is also applicable to other domains, and offers much of value for anyone building a real-world Bayes net. It is full of tips, techniques, tricks, and examples, and even includes a CD-ROM that has the entire book contents, example Bayes nets, and demonstration software (including an early version of Netica).
Download "BBN Guidelines -Cain.pdf" here.
Operational Risk: Measurement and Modelling, by Jack King.
In this book Jack King, PhD, provides a modern view of operational risk by focusing on its measurement and modelling, and by using techniques such as causal modelling and Bayes nets (with Netica as its example software). It contains theoretical and practical material and is applicable to the financial sector and beyond.
Order from Amazon, or from the publisher Wiley.

Bruce Marcot		 Bruce Marcot, a wildlife ecologist and researcher, has been applying Netica to ecological modelling for several years and written many papers. Of note are 2 of his most recent publications:
  • Marcot, B. G. 2006. Characterizing species at risk I: modeling rare species under the Northwest Forest Plan. Ecology and Society 11(2):10.
    View article online or in PDF with appendix.

  • Marcot, B. G., P. A. Hohenlohe, S. Morey, R. Holmes, R. Molina, M. Turley, M. Huff, and J. Laurence. 2006. Characterizing species at risk II: using Bayesian belief networks as decision support tools to determine species conservation categories under the Northwest Forest Plan. Ecology and Society 11(2):12.
    View article online or in PDF .

Real Time Decision Support System for Portfolio Management

by Chiu-Che Tseng and

Piotr J. Gmytrasiewicz

 From the Abstract: "We describe our real time decision support system; a system that supports information gathering and managing of an investment portfolio. Our system uses the Object Oriented Bayesian Knowledge Base (OOBKB) design to create a decision model at the most suitable level of detail to guide the information gathering activities, and to produce investment recommendation within a reasonable time. To determine the suitable level of detail we define and use the notion of urgency, or the value of time. Using it, our system can trade off the quality of support the model provides versus the cost of using the model at a particular level of detail. The decision models our system uses are implemented as influence diagrams. Using a suitable influence diagram, our system computes the value of consulting the various information sources available on the web, uses web agents to fetch the most valuable information, and evaluates the influence diagram producing the buy, sell and hold recommendations."

Réseaux bayésiens,

by Patrick Naïm et. al.

 By Patrick Naïm , Pierre-Henri Wuillemin , Philippe Leray , Olivier Pourret , and Anna Becker.
Translated from the Publisher's website: "In combining statistical and AI, Bayesian networks make it possible to analyze quantities of data useful for decision-making, control or prediction. Comprised of an "intuitive" presentation and a talk of the theoretical bases, the work is supplemented by a resolutely practical part (methodology of implementation, a panorama of the applicability, and three detailed case studies)."

Introductory References for Bayes Nets and Decision Nets

Collection of papers on GIS and Bayes Net