Introductory: For guidance on choosing Bayes net literature, and some descriptions of the below, see Introductory References.
Where Mentioned: Each bibliography reference is also listed in the index, so you can find where in this document it is mentioned.
Boerlage, Brent (1994) Link Strength in Bayesian Networks, UBC, British Columbia, Canada. See: Tech Report
Cain, Jeremy (2001) Planning Improvements in Natural Resource Management, Centre for Ecology & Hydrology, Wallingford UK. PDF
Charniak, Eugene (1991) “Bayesian networks without tears” in AI Magazine (Winter 1991), 12(4), 50-63.
Clemen, Robert and Terence Reilly (2001) Making Hard Decisions with DecisionTools, Brooks/Cole, Pacific Grove, CA.
Cowell, Robert, A. Philip Dawid, Steffen L. Lauritzen and David J. Spiegelhalter (1999) Probabilistic Networks and Expert Systems, Springer, New York.
Darwiche, Adnan (2009) Modeling and Reasoning with Bayesian Networks, Cambridge University Press.
Friedman, Nir, Dan Geiger, and Moises Goldszmidt (1997) "Bayesian network classifiers" in Machine Learning, Vol 29, 131-163. See: online PDF
Glymour, Clark (2001) The Mind’s Arrows: Bayes Nets and Graphical Causal Models in Psychology, The MIT Press, Cambridge, MA.
Heckerman, David and Jack Breese (1994) “A new look at causal independence” in Uncertainty in Artificial Intelligence: Proc. of the Tenth Conf. (July, Seattle, WA), Ramon Lopez de Mantaras and David Poole (eds.), Morgan Kaufmann, San Mateo, CA.
Heckerman, David, Abe Mamdani and Michael P. Wellman (1995) “Real-world applications of Bayesian networks” in Communications of the ACM, 38(3), 24-26. Introduction to this special issue of CACM on Bayes nets.
Henrion, Max, John S. Breese and Eric J. Horvitz (1991) “Decision analysis and expert systems” in AI Magazine (Winter 1991), 12(4), 64-91.
Jensen, Finn V. (1996) An Introduction to Bayesian Networks, Springer-Verlag, New York.
Jensen, Finn V. and Thomas D. Nielsen (2007) Bayesian Networks and Decision Graphs, 2nd Edition, Springer, New York.
Jensen, Frank, Finn V. Jensen and Soren L. Dittmer (1994) "From influence diagrams to junction trees" in Uncertainty in Artificial Intelligence: Proc. of the Tenth Conf. (July, Seattle, WA), Ramon Lopez de Mantaras and David Poole (eds.), Morgan Kaufmann, San Mateo, CA.
King, Jack L. (2001) Operational Risk: Measurement and Modelling, Wiley.
Kjaerulff, Uffe B. and Anders L. Madsen (2008) Bayesian Networks and Influence Diagrams, Springer, New York.
Koller, Daphne and Nir Friedman (2009) Probabilistic Graphical Models: Principles and Techniques, The MIT Press, Cambridge, MA.
Korb, Kevin and Ann E. Nicholson (2010) Bayesian Artificial Intelligence, Second Edition. Chapman & Hall, London, UK.
Lauritzen, Steffen L. (1995) "The EM algorithm for graphical association models with missing data" in Computational Statistics and Data Analysis, 19(2), 191-201.
Lauritzen, Steffen L. and David J. Spiegelhalter (1988) “Local computations with probabilities on graphical structures and their application to expert systems” in J. Royal Statistics Society B, 50(2), 157-194.
Matheson, James E. (1990) “Using Influence diagrams to value information and control” in Influence Diagrams, Belief Nets and Decision Analysis, Robert M. Oliver and J. Q. Smith (eds.), John Wiley & Sons, Chichester.
Neapolitan, Richard E. (1990) Probabilistic Reasoning in Expert Systems: Theory and Algorithms , John Wiley & Sons, New York. Currently out of print.
Neapolitan, Richard E. (2004) Learning Bayesian Networks , Pearson Prentice Hall, Upper Saddle River, NJ.
Neapolitan, Richard (2007) Probabilistic Methods for Financial and Marketing Informatics, Morgan Kaufmann Publishers.
Neapolitan, Richard (2009) Probabilistic Methods for Bioinformatics: With an Introduction to Bayesian Networks, Morgan Kaufmann Publishers.
NRC-CNRC (2006) Canadian Journal of Forest Research, Vol 36, Number 12, December 2006.
Pearl, Judea (1988) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Mateo, CA. 2nd edition 1991.
Pearl, Judea (2009) Causality- Models, Reasoning, and Inference, 2nd edition, Cambridge University Press, Cambridge, UK.
Pourret, Olivier, Patrick Naim and Bruce Marcot (2008) Bayesian Networks: A Practical Guide to Applications, Wiley.
Russell, Stuart and Peter Norvig (2009) Artificial Intelligence: A Modern Approach (3rd edition), Prentice Hall, Englewood Cliffs, NJ.
Shachter, Ross D. (1986) “Evaluating influence diagrams” in Operations Research, 34(6), 871-882.
Shachter, Ross D. (1988) “Probabilistic inference and influence diagrams” in Operations Research, 36(4), 589-604.
Shachter, Ross D. (1989) “Evidence absorption and propagation through evidence reversals” in Proc. of the Fifth Workshop on Uncertainty in Artificial Intelligence (Windsor, Ont.), 303-308. Later republished in: Henrion, Max (ed.) (1991) Uncertainty in Artificial Intelligence 5, North-Holland, Amsterdam.
Shafer, Glenn (1996) The Art of Causal Conjecture, MIT, MA, USA.
Sloman, Steven A. (2005) Causal Models: How People Think about the World and Its Alternatives, Oxford University Press, NY, USA.
Smith, James E., Samuel Holtzman and James E. Matheson (1993) “Structuring conditional relationships in influence diagrams” in Operations Research, 41(2), 280-297.
Spiegelhalter, David J., A. Philip Dawid, Steffen L. Lauritzen and Robert G. Cowell (1993) “Bayesian analysis in expert systems” in Statistical Science, 8(3), 219-283.
Spirtes, Peter, Clark Glymour and Richard Scheines (2000) Causation, Prediction, and Search Second Edition, The MIT Press, Cambridge, MA.
Zhang, Lianwen (Nevin), Runping Qi and David Poole (1994) “A computational theory of decision networks” in International Journal of Approximate Reasoning, 11(2), 83-158.