10 citations · 32 across the 16 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 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…
Weighted Programming
Kevin Batz, Adrian Gallus, Benjamin Lucien Kaminski +2
We study weighted programming, a programming paradigm for specifying mathematical models. More specifically, the weighted programs we investigate are like usual imperative programs…