most citedIterative Join-Graph Propagation

70 citations · 493 across the 25 of their papers we have counts for

collaborators

25 papers

cs.AI201356 cited

Identifying Independencies in Causal Graphs with Feedback

Judea Pearl, Rina Dechter

We show that the d -separation criterion constitutes a valid test for conditional independence relationships that are induced by feedback systems involving discrete variables.

cs.AI201318 cited

An Evaluation of Structural Parameters for Probabilistic Reasoning: Results on Benchmark Circuits

Yousri El Fattah, Rina Dechter

Many algorithms for processing probabilistic networks are dependent on the topological properties of the problem's structure. Such algorithms (e.g., clustering, conditioning) are e…

cs.AI201323 cited

Topological Parameters for Time-Space Tradeoff

Rina Dechter

In this paper we propose a family of algorithms combining tree-clustering with conditioning that trade space for time. Such algorithms are useful for reasoning in probabilistic and…

cs.AI20137 cited

Bucket Elimination: A Unifying Framework for Several Probabilistic Inference

Rina Dechter

Probabilistic inference algorithms for finding the most probable explanation, the maximum aposteriori hypothesis, and the maximum expected utility and for updating belief are refor…

cs.AI201366 cited

A Scheme for Approximating Probabilistic Inference

Rina Dechter, Irina Rish

This paper describes a class of probabilistic approximation algorithms based on bucket elimination which offer adjustable levels of accuracy and efficiency. We analyze the approxim…

cs.AI20139 cited

Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding

Irina Rish, Kalev Kask, Rina Dechter

It was recently shown that the problem of decoding messages transmitted through a noisy channel can be formulated as a belief updating task over a probabilistic network [McEliece].…