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Probabilistic programming languages are a flexible tool for specifying statistical models, but this flexibility comes at the cost of efficient analysis. It is currently difficult to compactly represent the subtle independence properties of a probabilistic program, and exploit independence properties to decompose inference. Classical graphical model abstractions do capture some properties of the underlying distribution, enabling inference algorithms to operate at the level of the graph topology. However, we observe that graph-based abstractions are often too coarse to capture interesting properties of programs. We propose a form of sound abstraction for probabilistic programs wherein the abstractions are themselves simplified programs. We provide a theoretical foundation for these abstractions, as well as an algorithm to generate them. Experimentally, we also illustrate the practical benefits of our framework as a tool to decompose probabilistic program inference.
Author Information
Steven Holtzen (University of California, Los Angeles)
Guy Van den Broeck (University of California, Los Angeles)
Todd Millstein (University of California, Los Angeles)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Oral: Sound Abstraction and Decomposition of Probabilistic Programs »
Fri. Jul 13th 07:50 -- 08:00 AM Room A4
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