Scaling up Probabilistic Inference in Linear and Non-Linear Hybrid Domains by Leveraging Knowledge Compilation.

Anton Fuxjaeger, Vaishak Belle

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

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
Original languageEnglish
Number of pages9
Publication statusPublished - 7 Feb 2020
EventNinth International Workshop on Statistical Relational AI - New York, United States
Duration: 7 Feb 20207 Feb 2020
Conference number: 9


WorkshopNinth International Workshop on Statistical Relational AI
Abbreviated titleStarAI 2020
Country/TerritoryUnited States
CityNew York
Internet address

Keywords / Materials (for Non-textual outputs)

  • Weighted Model Integration
  • Probabilistic Inference
  • Knowledge Compilation
  • Sentential Decision Diagrams
  • Satisfiability Modulo Theories


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