A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the
theoretical foundations and computational methods that underlie plausible reasoning under …

SUMMARY A causal network is used in a number of areas as a depiction of patterns of
'influence'among sets of variables. In expert systems it is common to perform 'inference'by …

Scientists often use directed acyclic graphs (dags) to model the qualitative struc ture of
causal theories, allowing the parameters to be estimated from observational data. Two …

Winner of the 2002 DeGroot Prize. Probabilistic expert systems are graphical networks that
support the modelling of uncertainty and decisions in large complex domains, while …

" Probabilistic risk analysis aims to quantify the risk caused by high technology installations.
Increasingly, such analyses are being applied to a wider class of systems in which problems …

Graphic modelling is a form of multivariate analysis that uses graphs to represent models.
These graphs display the structure of dependencies, both associational and causal …

An important feature of Bayesian networks is that they facilitate explicit encoding of
information about independencies in the domain, information that is indispensable for …

We investigate directed Markov fields over finite graphs without positivity assumptions on the
densities involved. A criterion for conditional independence of two groups of variables given …

This paper introduces and investigates the notion of a hyper Markov law, which is a
probability distribution over the set of probability measures on a multivariate space that (i) is …