Performing Bayesian Inference by Weighted Model Counting

By Tian Sang, Paul Beame, Henry A. Kautz. AAAI Conference on Artificial Intelligence, 2005

299 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7ec1b68b05dba4473127daab4d74f7214cf10424

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Citations in the corpus

A Semantic Loss Function for Deep Learning with Symbolic Knowledge (2017, methodology): “Examining Deﬁnition 1 of semantic loss, we see that the right-hand side is a well-known automated reasoning task called weighted model counting (WMC) (Chavira & Darwiche, 2008; Sang et al., 2005).”
Open-Universe Weighted Model Counting (2017, methodology): “Exact solvers are based on knowledge compilation (Chavira and Darwiche 2008) or exhaustive DPLL search (Sang, Beame, and Kautz 2005); approximate ones use local search (Wei and Selman 2005) or sampling (Ermon et al. 2013; Chakraborty et al. 2014).”
Probabilistic models with unknown objects (2006, methodology): “or weighted model counting on propositional formulas [Sang et al., 2005].”
Importance sampling-based estimation over AND/OR search spaces for graphical models (2012, methodology): “The grid networks are available from the authors of Cachet, a SAT model counter (Sang, Beame, and Kautz, 2005).”
Component Caching in Hybrid Domains with Piecewise Polynomial Densities (2016, methodology): “Given the maturity of propositional model counting technology, it is perhaps more immediate to consider the conditions under which we can leverage existing CC systems.”
Three Modern Roles for Logic in AI (2020, methodology): “,¬C. We can now compute the probability of any Boolean expression αby simply computing the weighted model count of ∆ ∧α, which completes the reduction of MAR to WMC. Another reduction was proposed in [73] which is suited towards Bayesiannetworksthathavevariableswithlargecardinalities.More refined re”
On the Tractability of SHAP Explanations (2020, methodology): “, Slack et al. (2020). We believe that more focus should be given to the computational complexity of SHAP explanations.”
Probabilistic Inference in Hybrid Domains by Weighted Model Integration (2015, methodology): “7We remark that our implementation does not yet consider the full range of effective WMC techniques [Sang et al., 2005], such as component caching [Bacchus et al.”