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Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations
2020
Neural Information Processing Systems
Weighted model integration (WMI) is a framework to perform advanced probabilistic inference in hybrid domains, i.e., on distributions over mixed continuous-discrete random variables and in the presence of complex logical and arithmetic constraints. In this work, we advance the WMI framework on both the theoretical and algorithmic side. First, we trace the boundaries of tractability for WMI inference in terms of two key properties of a WMI problem's dependency structure: sparsity and diameter.
dblp:conf/nips/ZengMYVB20
fatcat:rdy33pjrpvb3vbgcdoygguty74