10 citations · 25 across the 12 of their papers we have counts for
12 papers
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…
Weighted Automata for Exact Inference in Discrete Probabilistic Programs
Dominik Geißler, Tobias Winkler
In probabilistic programming, the inference problem asks to determine a program's posterior distribution conditioned on its "observe" instructions. Inference is challenging, especi…
Verifying Sampling Algorithms via Distributional Invariants
Daniel Zilken, Kevin Batz, Joost-Pieter Katoen +1
This paper presents a Hoare-like veri cation framework for discrete probabilistic programs that we apply to two non-trivial sampling algorithms: Lumbroso's Fast Dice Roller and Saa…
Foundations for Deductive Verification of Continuous Probabilistic Programs: From Lebesgue to Riemann and Back
Kevin Batz, Joost-Pieter Katoen, Francesca Randone +1
We lay out novel foundations for the computer-aided verification of guaranteed bounds on expected outcomes of imperative probabilistic programs featuring (i) general loops, (ii) co…
J-P: MDP. FP. PP.: Characterizing Total Expected Rewards in Markov Decision Processes as Least Fixed Points with an Application to Operational Semantics of Probabilistic Programs (Technical Report)
Kevin Batz, Benjamin Lucien Kaminski, Christoph Matheja +1
Markov decision processes (MDPs) with rewards are a widespread and well-studied model for systems that make both probabilistic and nondeterministic choices. A fundamental result ab…
Markov Decision Processes with Sure Parity and Multiple Reachability Objectives
Raphaël Berthon, Joost-Pieter Katoen, Tobias Winkler
This paper considers the problem of finding strategies that satisfy a mixture of sure and threshold objectives in Markov decision processes. We focus on a single -regular object…