198 citations · 959 across the 31 of their papers we have counts for
5 papers · 1 filter
Asymptotic Model Selection for Naive Bayesian Networks
Dmitry Rusakov, Dan Geiger
We develop a closed form asymptotic formula to compute the marginal likelihood of data given a naive Bayesian network model with two hidden states and binary features. This formula…
Factorization of Discrete Probability Distributions
Dan Geiger, Christopher Meek, Bernd Sturmfels
We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or…
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,…
A Distance-Based Branch and Bound Feature Selection Algorithm
Ari Frank, Dan Geiger, Zohar Yakhini
There is no known efficient method for selecting k Gaussian features from n which achieve the lowest Bayesian classification error. We show an example of how greedy algorithms face…
Importance Sampling via Variational Optimization
Ydo Wexler, Dan Geiger
Computing the exact likelihood of data in large Bayesian networks consisting of thousands of vertices is often a difficult task. When these models contain many deterministic condit…