#### Filter Results:

- Full text PDF available (48)

#### Publication Year

1994

2017

- This year (0)
- Last 5 years (10)
- Last 10 years (25)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

Learn More

- David Heckerman, Dan Geiger, David Maxwell Chickering
- Machine Learning
- 1994

We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data. First and foremost, we develop a methodology for assessing informative… (More)

- David Maxwell Chickering
- Journal of Machine Learning Research
- 2002

In this paper we prove the so-called “Meek Conjecture”. In particular, we show that if a DAG H is an independence map of another DAG G, then there exists a finite sequence of edge additions and… (More)

- David Maxwell Chickering
- UAI
- 1996

Approaches to learning Bayesian networks from data typically combine a scoring metric with a heuristic search procedure. Given a Bayesian network structure, many of the scoring metrics derived in the… (More)

- David Maxwell Chickering
- AISTATS
- 1995

Algorithms for learning Bayesian networks from data have t wo components: a scoring metric and a search procedure. The scoring metric computes a score reeecting the goodness-of-t of the structure to… (More)

- David Maxwell Chickering
- UAI
- 1995

We present a simple characterization of equivalent Bayesian network structures based on local transformations. The signi cance of the characterization is twofold. First, we are able to easily prove… (More)

- David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Myers Kadie
- Journal of Machine Learning Research
- 2000

We describe a graphical model for probabilistic relationships|an alternative to the Bayesian network|called a dependency network. The graph of a dependency network, unlike a Bayesian network, is… (More)

- David Maxwell Chickering, David Heckerman
- Machine Learning
- 1997

We discuss Bayesian methods for model averaging and model selection among Bayesian-network models with hidden variables. In particular, we examine large-sample approximations for the marginal… (More)

- David Maxwell Chickering, David Heckerman, Christopher Meek
- Journal of Machine Learning Research
- 2003

In this paper, we provide new complexity results for algorit hms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a scoring criterion… (More)

Recently several researchers have investigated techniques for using data to learn Bayesian networks containing compact representations for the conditional probability distributions (CPDs) stored at… (More)

- Léon Bottou, Jonas Peters, +6 authors Ed Snelson
- Journal of Machine Learning Research
- 2013

This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such… (More)