389 citations · 2.3k across the 33 of their papers we have counts for
4 papers · 2 filters
Models and Selection Criteria for Regression and Classification
David Heckerman, Christopher Meek
When performing regression or classification, we are interested in the conditional probability distribution for an outcome or class variable Y given a set of explanatoryor input va…
Asymptotic Model Selection for Directed Networks with Hidden Variables
Dan Geiger, David Heckerman, Christopher Meek
We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be u…
A Bayesian Approach to Learning Bayesian Networks with Local Structure
David Maxwell Chickering, David Heckerman, Christopher Meek
Recently several researchers have investigated techniques for using data to learn Bayesian networks containing compact representations for the conditional probability distributions…
Learning Mixtures of DAG Models
Bo Thiesson, Christopher Meek, David Maxwell Chickering +1
We describe computationally efficient methods for learning mixtures in which each component is a directed acyclic graphical model (mixtures of DAGs or MDAGs). We argue that simple…