Resources > Research papers

While there a good number of excellent papers covering the topic of decision-analytic decision support, here are some of our favorites.

An introductory paper on Bayesian networks, useful for beginners is (Charniak, 1991).

Overview papers by Horvitz et al. (1988), Cooper (1989), Henrion et al. (1991), Spiegelhalter et al. (1993) and Matzkevich & Abramson (1995) are accessible introductions to the use of probabilistic and decision-analytic methods in decision support systems.

Users interested in practical applications of Bayesian networks are directed to the March 1995 special issue of the Communications of the ACM journal, edited by Heckerman, Mamdani and Wellman (1995).

(Howard, 1984) is a good introduction to influence diagrams, the book in which this paper has been published, is a good collection of reading on decision analysis.

(Henrion, 1988) is a manifesto arguing convincingly for the use of probabilistic methods in artificial intelligence.

(Henrion, 1989) is a practical introduction to problems related to building probabilistic models. Another place to look at is a special issue of the IEEE Transaction of Knowledge and Data Engineering journal on building probabilistic models (Druzdzel & van der Gaag, 2000).

Foundations of conditional independence on which graphical models are built are outlined in (Dawid 1979).

The principles of relevance reasoning are outlined in (Druzdzel & Suermondt, 1994).