Probabilistic Approximation Algorithms
Probabilistic Approximation Algorithms is an approximation algorithm that belongs to the class of randomized algorithms, distinguished by its use of randomness in the computational process to achieve an approximate solution with provable performance guarantees in expectation or with high probability.
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268
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2017–2026
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Generalized Belief Propagation
Jonathan S. Yedidia, William T. Freeman, Yair Weiss · 2000 · 916 citations
Mark Jerrum, Alistair Sinclair · SIAM Journal on Computing · 1989 · 793 citations
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Stanford University
Stanford, United States
10 h-index
18 publications
2.3K citations
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
6 publications
2.6K citations
University of California, Berkeley
Berkeley, United States
5 h-index
6 publications
1.1K citations
University of California, Irvine
Irvine, United States
5 h-index
9 publications
286 citations
University of Oxford
Oxford, United Kingdom
5 h-index
7 publications
337 citations

Cornell University
Ithaca, United States
5 h-index
6 publications
316 citations
Microsoft (United States)
Redmond, United States
4 h-index
6 publications
247 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
8 publications
623 citations
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Repository · 15 publications · 490 citations
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6 publications · 225 citations
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