# Bayesian network

## Papers overview

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Highly Cited

2009

Highly Cited

2009

- 2009

This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It providesâ€¦Â (More)

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Highly Cited

2006

Highly Cited

2006

- Machine Learning
- 2006

We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combinesâ€¦Â (More)

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Highly Cited

2004

Highly Cited

2004

- 2004

An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the stateâ€¦Â (More)

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Highly Cited

2003

Highly Cited

2003

- Machine Learning
- 2003

In many multivariate domains, we are interested in analyzing the dependency structure of the underlying distribution, e.gâ€¦Â (More)

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Highly Cited

1997

Highly Cited

1997

- UAI
- 1997

Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have beenâ€¦Â (More)

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Highly Cited

1996

Highly Cited

1996

- J. Artif. Intell. Res.
- 1996

A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A Bayesian network can beâ€¦Â (More)

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Highly Cited

1996

Highly Cited

1996

- UAI
- 1996

Approaches to learning Bayesian networks from data typically combine a scoring metric with a heuristic search procedure. Given aâ€¦Â (More)

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Highly Cited

1996

Highly Cited

1996

- UAI
- 1996

Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. Thisâ€¦Â (More)

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Highly Cited

1993

Highly Cited

1993

- Artif. Intell.
- 1993

This paper presents a simple framework for Horn clause abduc tion with probabilities associated with hypotheses The frameworkâ€¦Â (More)

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Highly Cited

1991

Highly Cited

1991

- AI Magazine
- 1991

50 AI MAGAZINE u n d e r s t a n d i n g (Charniak and Goldman 1989a, 1989b; Goldman 1990), vision (Levitt, Mullin, and Binfordâ€¦Â (More)

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