27 citations · 64 across the 13 of their papers we have counts for
8 papers · 1 filter
Multiobjective Preexpectation Reasoning for Probabilistic Programs
Lena Verscht, Hannah Mertens, Kevin Batz +3
Probabilistic programs with nondeterminism model planning problems in which a strategy resolves the nondeterminism to optimize an expected outcome. We study the multiobjective sett…
Caesar: A Deductive Verifier for Probabilistic Programs
Philipp Schröer, Kevin Batz, Umut Yiğit Dural +4
Caesar is a deductive verifier for probabilistic programs. At its core lies HeyVL, a quantitative intermediate verification language based on the real-valued logic HeyLo. HeyVL all…
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…
A Deductive Verification Infrastructure for Probabilistic Programs (Extended Version)
Philipp Schröer, Kevin Batz, Benjamin Lucien Kaminski +2
This paper presents a quantitative program verification infrastructure for discrete probabilistic programs. Our infrastructure can be viewed as the probabilistic analogue of Boogie…
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…
How long, O Bayesian network, will I sample thee? A program analysis perspective on expected sampling times
Kevin Batz, Benjamin Lucien Kaminski, Joost-Pieter Katoen +1
Bayesian networks (BNs) are probabilistic graphical models for describing complex joint probability distributions. The main problem for BNs is inference: Determine the probability…