• Publications
  • Influence
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
TLDR
We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data that makes use of a prior network. Expand
  • 3,444
  • 288
  • PDF
Optimal Structure Identification With Greedy Search
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 andExpand
  • 1,122
  • 210
  • PDF
Learning Equivalence Classes of Bayesian-Network Structures
TLDR
We describe a convenient graphical representation for an equivalence class of structures, and introduce a set of operators that can be applied to that representation by a search algorithm to move among equivalence classes. Expand
  • 649
  • 115
  • PDF
Learning Bayesian Networks is NP-Complete
TLDR
We show that the search problem of identifying a Bayesian network—among those where each node has at most K parents—that has a relative posterior probability greater than a given constant is NP-complete, when the BDe metric is used. Expand
  • 988
  • 72
  • PDF
Dependency Networks for Inference, Collaborative Filtering, and Data Visualization
TLDR
We describe a graphical model for probabilistic relationships--an alternative to the Bayesian network--called a dependency network. Expand
  • 573
  • 64
  • PDF
A Transformational Characterization of Equivalent Bayesian Network Structures
TLDR
We present a simple characterization of equivalent Bayesian network structures based on local transformations. Expand
  • 344
  • 59
  • PDF
Counterfactual reasoning and learning systems: the example of computational advertising
TLDR
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. Expand
  • 385
  • 40
  • PDF
A Bayesian Approach to Learning Bayesian Networks with Local Structure
TLDR
We apply a Bayesian approach to learning Bayesian networks that contain decision-graphs| generalizations of decision trees that can encode arbitrary equality constraints. Expand
  • 373
  • 35
  • PDF
Large-Sample Learning of Bayesian Networks is NP-Hard
TLDR
We provide new complexity results for algorithms that learn discretevariable Bayesian networks from data. Expand
  • 537
  • 33
  • PDF
Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables
TLDR
We discuss Bayesian methods for model averaging and model selection among Bayesian-network models with hidden variables in which the root node is hidden. Expand
  • 298
  • 22
  • PDF