S
Sebastian Junges
Researcher at University of California, Berkeley
Publications - 90
Citations - 2217
Sebastian Junges is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Markov decision process & Probabilistic logic. The author has an hindex of 20, co-authored 89 publications receiving 1668 citations. Previous affiliations of Sebastian Junges include Radboud University Nijmegen & RWTH Aachen University.
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Book ChapterDOI
A Storm is Coming: A Modern Probabilistic Model Checker
TL;DR: The new probabilistic model checker Storm features the analysis of discrete- and continuous-time variants of both Markov chains and MDPs and offers a Python API for rapid prototyping by encapsulating Storm’s fast and scalable algorithms.
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A storm is Coming: A Modern Probabilistic Model Checker
TL;DR: Storm as mentioned in this paper is a probabilistic model checker for both discrete and continuous-time variants of both Markov chains and MDPs that supports the PRISM and JANI modeling languages.
Book ChapterDOI
PROPhESY : A PRObabilistic ParamEter SYnthesis Tool
Christian Dehnert,Sebastian Junges,Nils Jansen,Florian Corzilius,Matthias Volk,Harold Bruintjes,Joost-Pieter Katoen,Erika Ábrahám +7 more
TL;DR: ProPhESY, a tool for analyzing parametric Markov chains (MCs), can compute a rational function (i.e., a fraction of two polynomials in the model parameters) for reachability and expected reward objectives and supports the novel feature of conditional probabilities.
Book ChapterDOI
Parameter Synthesis for Markov Models: Faster Than Ever
TL;DR: In this article, the authors propose a technique for verifying probabilistic models whose transition probabilities are parametric, replacing parametric transitions by non-deterministic choices of extremal values.
Book ChapterDOI
JANI: Quantitative Model and Tool Interaction
Carlos E. Budde,Christian Dehnert,Ernst Moritz Hahn,Arnd Hartmanns,Sebastian Junges,Andrea Turrini +5 more
TL;DR: The Jani model format and tool interaction protocol is a metamodel based on networks of communicating automata and has been designed for ease of implementation without sacrificing readability, to provide a stable and uniform interface between tools such as model checkers, transformers, and user interfaces.