1 research outputs found
Scaling up Probabilistic Inference in Linear and Non-Linear Hybrid Domains by Leveraging Knowledge Compilation.
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.Comment: In proceedings of ICAART, 2020. A version also appears in AAAI
Workshop: Statistical Relational Artificial Intelligence (StarAI), 202