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Poster
Probabilistic Generating Circuits
Honghua Zhang · Brendan Juba · Guy Van den Broeck

Thu Jul 22 09:00 PM -- 11:00 PM (PDT) @
in Poster Session 6 »

Generating functions, which are widely used in combinatorics and probability theory, encode function values into the coefficients of a polynomial. In this paper, we explore their use as a tractable probabilistic model, and propose probabilistic generating circuits (PGCs) for their efficient representation. PGCs are strictly more expressive efficient than many existing tractable probabilistic models, including determinantal point processes (DPPs), probabilistic circuits (PCs) such as sum-product networks, and tractable graphical models. We contend that PGCs are not just a theoretical framework that unifies vastly different existing models, but also show great potential in modeling realistic data. We exhibit a simple class of PGCs that are not trivially subsumed by simple combinations of PCs and DPPs, and obtain competitive performance on a suite of density estimation benchmarks. We also highlight PGCs' connection to the theory of strongly Rayleigh distributions.

Keywords: Graphical Models

Author Information

Honghua Zhang (University of California, Los Angeles)
Brendan Juba (Washington University in St Louis)
Guy Van den Broeck (University of California, Los Angeles)

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