26 citations · 45 across the 4 of their papers we have counts for
4 papers
Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling
Ru He, Jin Tian, Huaiqing Wu
We study the Bayesian model averaging approach to learning Bayesian network structures (DAGs) from data. We develop new algorithms including the first algorithm that is able to eff…
Bayesian Mixture Models for Frequent Itemset Discovery
Ruefei He, Jonathan Shapiro
In binary-transaction data-mining, traditional frequent itemset mining often produces results which are not straightforward to interpret. To overcome this problem, probability mode…
Computing Posterior Probabilities of Structural Features in Bayesian Networks
Jin Tian, Ru He
We study the problem of learning Bayesian network structures from data. Koivisto and Sood (2004) and Koivisto (2006) presented algorithms that can compute the exact marginal poster…
Bayesian Model Averaging Using the k-best Bayesian Network Structures
Jin Tian, Ru He, Lavanya Ram
We study the problem of learning Bayesian network structures from data. We develop an algorithm for finding the k-best Bayesian network structures. We propose to compute the poster…