312 citations · 649 across the 29 of their papers we have counts for
Showing 2013Show all
3 papers · 1 filter
cs.AI2013★ 55 cited
Learning Bayesian Nets that Perform Well
Russell Greiner, Adam J. Grove, Dale Schuurmans
A Bayesian net (BN) is more than a succinct way to encode a probabilistic distribution; it also corresponds to a function used to answer queries. A BN can therefore be evaluated by…
cs.LG2013★ 77 cited
Comparing Bayesian Network Classifiers
Jie Cheng, Russell Greiner
In this paper, we empirically evaluate algorithms for learning four types of Bayesian network (BN) classifiers - Naive-Bayes, tree augmented Naive-Bayes, BN augmented Naive-Bayes a…
cs.AI2013★ 18 cited
Bayesian Error-Bars for Belief Net Inference
Tim Van Allen, Russell Greiner, Peter Hooper
A Bayesian Belief Network (BN) is a model of a joint distribution over a setof n variables, with a DAG structure to represent the immediate dependenciesbetween the variables, and a…