Probabilistic Inference in Hybrid Domains by Weighted Model Integration

123 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Hashing-Based Approximate Probabilistic Inference in Hybrid Domains (2015, methodology): “Each instance of VOL(M,w) involves the following (Belle et al., 2015): Proposition 13: Suppose ∆ is an SMT theory over continuous variables X, M a model of ∆−, and w is as above.”
Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series (2016, background): “SRL models for continuous variables (Wang & Domingos, 2008; Choi et al., 2010; Belle et al., 2015; Choi et al., 2015) and lifted inference algorithms have been proposed (Kimmig et al.”
Weighted Model Counting With Function Symbols (2017, background): “In that vein, logical languages with arithmetic constraints have been shown to be useful for probabilistic reasoning in mixed discrete-continuous spaces [Sanner and Abbasnejad, 2012, Belle et al., 2015, Alberti et al., 2016, de Salvo Braz et al., 2016].”
Component Caching in Hybrid Domains with Piecewise Polynomial Densities (2016, methodology): “In previous work (Belle, Passerini, and Van den Broeck 2015), we proposed a formulation of this problem as a model counting task over linear arithmetic formulas followed by an integration over the weights of models, referred to as weighted model integration (WMI).”