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- W4289238082 abstract "Weighted model integration (WMI) extends weighted model counting (WMC) in providing a computational abstraction for probabilistic inference in mixed discrete-continuous domains. WMC has emerged as an assembly language for state-of-the-art reasoning in Bayesian networks, factor graphs, probabilistic programs and probabilistic databases. In this regard, WMI shows immense promise to be much more widely applicable, especially as many real-world applications involve attribute and feature spaces that are continuous and mixed. Nonetheless, state-of-the-art tools for WMI are limited and less mature than their propositional counterparts. In this work, we propose a new implementation regime that leverages propositional knowledge compilation for scaling up inference. In particular, we use sentential decision diagrams, a tractable representation of Boolean functions, as the underlying model counting and model enumeration scheme. Our regime performs competitively to state-of-the-art WMI systems but is also shown to handle a specific class of non-linear constraints over non-linear potentials." @default.
- W4289238082 created "2022-08-01" @default.
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- W4289238082 creator A5086803196 @default.
- W4289238082 date "2018-11-29" @default.
- W4289238082 modified "2023-09-27" @default.
- W4289238082 title "Scaling up Probabilistic Inference in Linear and Non-Linear Hybrid Domains by Leveraging Knowledge Compilation" @default.
- W4289238082 doi "https://doi.org/10.48550/arxiv.1811.12127" @default.
- W4289238082 hasPublicationYear "2018" @default.
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