This paper surveys methods for representing and reasoning with imperfect information. It
opens with an attempt to classify the different types of imperfection that may pervade data …

Computational Intelligence comprises concepts, paradigms, algorithms, and
implementations of systems that are supposed to exhibit intelligent behavior in complex …

The authors address the assumptions and methods that allow us to turn observations into
causal knowledge, and use even incomplete causal knowledge in planning and prediction …

This new edition continues to serve as a comprehensive guide to modern and classical
methods of statistical computing. The book is comprised of four main parts spanning the …

We review recent developments in applying Bayesian probabilistic and statistical ideas to
expert systems. Using a real, moderately complex, medical example we illustrate how …

Given n discrete random variables/spl Omega/={X/sub 1/,/spl middot//spl middot//spl middot/,
X/sub n/}, associated with any subset/spl alpha/of (1, 2,/spl middot//spl middot//spl middot …

Conditional independence is a topic that lies between statistics and artificial intelligence.
Probabilistic Conditional Independence Structures provides the mathematical description of …

Directed acyclic graph (DAG) models are popular tools for describing causal relationships
and for guiding attempts to learn them from data. They appear to supply a means of …

Given a collection of random variables [ξ i] i∈ N where N is a finite nonempty set, the
corresponding multiinformation function assigns to each subset A⊂ N the relative entropy of …

From the Publisher: The concept of modelling using graph theory has its origin in several
scientific areas, notably statistics, physics, genetics, and engineering. The use of graphical …