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Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Yi Wu · Siddharth Srivastava · Nicholas Hay · Simon Du · Stuart Russell

Thu Jul 12 05:20 AM -- 05:30 AM (PDT) @ A4

Despite the recent successes of probabilistic programming languages (PPLs) in AI applications, PPLs offer only limited support for random variables whose distributions combine discrete and continuous elements. We develop the notion of measure-theoretic Bayesian networks (MTBNs) and use it to provide more general semantics for PPLs with arbitrarily many random variables defined over arbitrary measure spaces. We develop two new general sampling algorithms that are provably correct under the MTBN framework: the lexicographic likelihood weighting (LLW) for general MTBNs and the lexicographic particle filter (LPF), a specialized algorithm for state-space models. We further integrate MTBNs into a widely used PPL system, BLOG, and verify the effectiveness of the new inference algorithms through representative examples.

Author Information

Yi Wu (UC Berkeley)
Siddharth Srivastava (Arizona State University)
Nicholas Hay
Simon Du (Carnegie Mellon University)
Stuart Russell (UC Berkeley)

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