"These papers represent two of the many different graphical modeling camps that have emerged from a flurry of activity in the past decade. The paper by Cox and Wermuth falls within the statistical graphical modeling camp and provides a useful generalization of that body of work. There is, of course, a price to be paid for this generality, namely that the interpretation of the graphs is more complex...The paper by Spiegelhalter, Dawid, Lauritzen and Cowell falls within the probabilistic expert system camp. This is a tour de force by researchers responsible for much of the astonishing progress in this area. Ten years ago, probabilistic models were shunned by the artificial intelligence community. That they are now widely accepted and used is ...
This is a chapter for the book "Bayesian Methods and Expert Elicitation" edited by Klaus Bocker, 23 ...
In this chapter, we introduce some of the tools that can be used to address these challenges. By con...
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...
We review recent developments in applying Bayesian probabilistic and statistical ideas to expert sys...
Probabilistic graphical models constitute a fundamental tool for the development of intelligent sys...
In this paper, we review the role of probabilistic graphical models in artificial intelligence. We s...
Winner of the 2002 DeGroot Prize.Probabilistic expert systems are graphical networks that support th...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
Probabilistic models based on directed acyclic graphs (DAGs) have a long and rich tradition, which b...
Inference under uncertainty plays a crucial role in expert system and receives growing attention fro...
© 2015, The Author(s). Recent decades have seen enormous improvements in computational inference for...
"'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent th...
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, prov...
In this article, which is a supplementary article to the TICS July Special Issue on probabilistic mo...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...
This is a chapter for the book "Bayesian Methods and Expert Elicitation" edited by Klaus Bocker, 23 ...
In this chapter, we introduce some of the tools that can be used to address these challenges. By con...
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...
We review recent developments in applying Bayesian probabilistic and statistical ideas to expert sys...
Probabilistic graphical models constitute a fundamental tool for the development of intelligent sys...
In this paper, we review the role of probabilistic graphical models in artificial intelligence. We s...
Winner of the 2002 DeGroot Prize.Probabilistic expert systems are graphical networks that support th...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
Probabilistic models based on directed acyclic graphs (DAGs) have a long and rich tradition, which b...
Inference under uncertainty plays a crucial role in expert system and receives growing attention fro...
© 2015, The Author(s). Recent decades have seen enormous improvements in computational inference for...
"'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent th...
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, prov...
In this article, which is a supplementary article to the TICS July Special Issue on probabilistic mo...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...
This is a chapter for the book "Bayesian Methods and Expert Elicitation" edited by Klaus Bocker, 23 ...
In this chapter, we introduce some of the tools that can be used to address these challenges. By con...
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...