198 citations · 959 across the 31 of their papers we have counts for
7 papers · 1 filter
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…
An Entropy-based Learning Algorithm of Bayesian Conditional Trees
Dan Geiger
This article offers a modification of Chow and Liu's learning algorithm in the context of handwritten digit recognition. The modified algorithm directs the user to group digits int…
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…
Graphical Models and Exponential Families
Dan Geiger, Christopher Meek
We provide a classification of graphical models according to their representation as subfamilies of exponential families. Undirected graphical models with no hidden variables are l…
Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions
Dan Geiger, David Heckerman
We show that the only parameter prior for complete Gaussian DAG models that satisfies global parameter independence, complete model equivalence, and some weak regularity assumption…
Automated Analytic Asymptotic Evaluation of the Marginal Likelihood for Latent Models
Dmitry Rusakov, Dan Geiger
We present and implement two algorithms for analytic asymptotic evaluation of the marginal likelihood of data given a Bayesian network with hidden nodes. As shown by previous work,…