198 citations · 959 across the 31 of their papers we have counts for
31 papers
Likelihoods and Parameter Priors for Bayesian Networks
David Heckerman, Dan Geiger
We develop simple methods for constructing likelihoods and parameter priors for learning about the parameters and structure of a Bayesian network. In particular, we introduce sever…
Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions
Dan Geiger, David Heckerman
We develop simple methods for constructing parameter priors for model choice among Directed Acyclic Graphical (DAG) models. In particular, we introduce several assumptions that per…
Dependence and Relevance: A probabilistic view
Dan Geiger, David Heckerman
We examine three probabilistic concepts related to the sentence "two variables have no bearing on each other". We explore the relationships between these three concepts and establi…
Accurate Liability Estimation Improves Power in Ascertained Case Control Studies
Omer Weissbrod, Christoph Lippert, Dan Geiger +1
Linear mixed models (LMMs) have emerged as the method of choice for confounded genome-wide association studies. However, the performance of LMMs in non-randomly ascertained case-co…
Random Algorithms for the Loop Cutset Problem
Ann Becker, Reuven Bar-Yehuada, Dan Geiger
We show how to find a minimum loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the first step in Pearl's method of conditioning for inference.…
Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (1997)
Dan Geiger, Prakash Shenoy
This is the Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, which was held in Providence, RI, August 1-3, 1997