Research map / Probabilistic and scientific modeling / Variational and generative sampling
Sound Abstraction and Decomposition of Probabilistic Programs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Decompose exact inference via distributionally sound predicate abstraction (abstract program + sub-queries) instead of inferring on the concrete program | Multiplication probabilistic program (Psi exact inference) · Psi exact inference, Figure 2a | total inference time (s) | Helped |
| Decompose exact inference via abstraction (recursive predicate abstraction on if-guards) | Markov chain probabilistic program · Psi exact inference, Figure 2a | total inference time (s) | Helped |
| Abstract exchangeable cards into a {1} vs not-{1} predicate abstraction | Card shuffle probabilistic program · Psi exact inference, Figure 2a | total inference time (s) | Helped |
| Hybrid decomposed MCMC (abstraction with predicates {x<k}, {yi<c}, enumeration on abstract program) instead of MCMC on the un-abstracted model | Three-observation noisy model (approximate inference) · MCMC log l1 error vs number of samples, Figure 2b | log l1 error (lower is better) | Helped |
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