David Maxwell Chickering

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Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

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)

Optimal Structure Identification With Greedy Search

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)

Learning Equivalence Classes of Bayesian Network Structures

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)

Learning Bayesian Networks is NP-Complete

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)

A Transformational Characterization of Equivalent Bayesian Network Structures

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)

Dependency Networks for Inference, Collaborative Filtering, and Data Visualization

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)

Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables

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)

Large-Sample Learning of Bayesian Networks is NP-Hard

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)

A Bayesian Approach to Learning Bayesian Networks with Local Structure

David Maxwell Chickering, David Heckerman, Christopher Meek
UAI
1997

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)

Counterfactual reasoning and learning systems: the example of computational advertising

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)

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