35 citations · 58 across the 4 of their papers we have counts for
4 papers · 1 filter
Dual Decomposition from the Perspective of Relax, Compensate and then Recover
Arthur Choi, Adnan Darwiche
Relax, Compensate and then Recover (RCR) is a paradigm for approximate inference in probabilistic graphical models that has previously provided theoretical and practical insights o…
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…
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…