35 citations · 58 across the 4 of their papers we have counts for
4 papers
A Variational Approach for Approximating Bayesian Networks by Edge Deletion
Arthur Choi, Adnan Darwiche
We consider in this paper the formulation of approximate inference in Bayesian networks as a problem of exact inference on an approximate network that results from deleting edges (…
Node Splitting: A Scheme for Generating Upper Bounds in Bayesian Networks
Arthur Choi, Mark Chavira, Adnan Darwiche
We formulate in this paper the mini-bucket algorithm for approximate inference in terms of exact inference on an approximate model produced by splitting nodes in a Bayesian network…
Approximating the Partition Function by Deleting and then Correcting for Model Edges
Arthur Choi, Adnan Darwiche
We propose an approach for approximating the partition function which is based on two steps: (1) computing the partition function of a simplified model which is obtained by deletin…
EDML: A Method for Learning Parameters in Bayesian Networks
Arthur Choi, Khaled S. Refaat, Adnan Darwiche
We propose a method called EDML for learning MAP parameters in binary Bayesian networks under incomplete data. The method assumes Beta priors and can be used to learn maximum likel…