Authors:
Anton R. Fuxjaeger
1
and
Vaishak Belle
2
;
1
Affiliations:
1
University of Edinburgh, U.K.
;
2
Alan Turing Institute, U.K.
Keyword(s):
Weighted Model Integration, Probabilistic Inference, Knowledge Compilation, Sentential Decision Diagrams, Satisfiability Modulo Theories.
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 b
ut is also shown to handle a specific class of non-linear constraints over non-linear potentials.
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