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Highly Cited

2008

Highly Cited

2008

Reasoning with incomplete and unreliable information is a central characteristic of decision making, for example in industry…

Highly Cited

2007

Highly Cited

2007

Preface. I. BASICS. 1. Introduction to Bayesian Networks. 2. More DAG/Probability Relationships. II. INFERENCE. 3. Inference…

Highly Cited

2006

Highly Cited

2006

We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines…

Highly Cited

2004

Highly Cited

2004

Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of…

Review

2004

Review

2004

We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data. First…

Highly Cited

2004

Highly Cited

2004

In many multivariate domains, we are interested in analyzing the dependency structure of the underlying distribution, e.g…

Highly Cited

2001

Highly Cited

2001

Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under…

Review

1998

Review

1998

A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in…

Highly Cited

1996

Highly Cited

1996

Computational modelling of probability has become a major part of automated decision support systems. In this book, the principal…

Highly Cited

1995

Highly Cited

1995

Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions…