The literature review presented discusses different methods under the general rubric of
learning Bayesian networks from data, and includes some overlapping work on more …

Probabilistic graphical models and decision graphs are powerful modeling tools for
reasoning and decision making under uncertainty. As modeling languages they allow a …

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 …

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

Students are characterized by different learning styles, focusing on different types of
information and processing this information in different ways. One of the desirable …

Probabilistic networks (also known as Bayesian belief networks) allow a compact
description of complex stochastic relationships among several random variables. They are …

In this paper we present eTeacher, an intelligent agent that provides personalized
assistance to e-learning students. eTeacher observes a student's behavior while he/she is …

Reasoning with uncertainty is more common than reasoning without. Based on just a limited
number of observed events we decide to perform an action. However, the events that we …

Spiegelhalter and Lauritzen [15] studied sequential learning in Bayesian networks and
proposed three models for the representation of conditional probabilities. A forth model …

The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an
accessible and student friendly introduction to AI. This fully revised and expanded update …