99 citations · 115 across the 7 of their papers we have counts for
3 papers · 1 filter
Generating Functions Meet Occupation Measures: Invariant Synthesis for Probabilistic Loops (Extended Version)
Darion Haase, Kevin Batz, Adrian Gallus +4
A fundamental computational task in probabilistic programming is to infer a program's output (posterior) distribution from a given initial (prior) distribution. This problem is cha…
Exact Bayesian Inference for Loopy Probabilistic Programs using Generating Functions
Lutz Klinkenberg, Christian Blumenthal, Mingshuai Chen +2
We present an exact Bayesian inference method for inferring posterior distributions encoded by probabilistic programs featuring possibly unbounded loops. Our method is built on a d…
Exact Probabilistic Inference Using Generating Functions
Lutz Klinkenberg, Tobias Winkler, Mingshuai Chen +1
Probabilistic programs are typically normal-looking programs describing posterior probability distributions. They intrinsically code up randomized algorithms and have long been at…