Generalized ranking supermartingales witness uniqueness of fixed points and thereby enable unified lower-bound verification for termination probability, weakest preexpectation, expected runtime, higher moments, and conditional weakest preexpectation in probabilistic programs.
Semantics of Probabilistic Programs
2 Pith papers cite this work, alongside 83 external citations. Polarity classification is still indexing.
2
Pith papers citing it
83
external citations · OpenAlex
verdicts
UNVERDICTED 2representative citing papers
Reactive graphs enable efficient MCMC inference in probabilistic programming languages by automatically tracking and selectively recomputing data dependencies during sampling.
citing papers explorer
-
Supermartingales for Unique Fixed Points: A Unified Approach to Lower Bound Verification
Generalized ranking supermartingales witness uniqueness of fixed points and thereby enable unified lower-bound verification for termination probability, weakest preexpectation, expected runtime, higher moments, and conditional weakest preexpectation in probabilistic programs.
-
Reactive Graphs for Efficient Markov Chain Monte Carlo Inference in Probabilistic Programming Languages
Reactive graphs enable efficient MCMC inference in probabilistic programming languages by automatically tracking and selectively recomputing data dependencies during sampling.