J
Joost-Pieter Katoen
Researcher at RWTH Aachen University
Publications - 488
Citations - 20723
Joost-Pieter Katoen is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: Probabilistic logic & Markov chain. The author has an hindex of 63, co-authored 461 publications receiving 19043 citations. Previous affiliations of Joost-Pieter Katoen include University of Erlangen-Nuremberg & University of Twente.
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Monitoring CTMCs by Multi-clock Timed Automata
TL;DR: A numerical algorithm is presented to solve an ordinary differential equation (ODE) that exploits the specific characteristics of the product EPDP to verify continuous-time Markov chains (CTMCs) against multi-clock deterministic timed automata (DTA).
Journal ArticleDOI
DFT modeling approach for operational risk assessment of railway infrastructure
TL;DR: In this paper , the authors present a framework based on dynamic fault trees that allows to analyze train routability based on train paths projected in the interlocking system, focusing on the dependency of train paths on track-based assets such as switches and crossings.
Posted Content
Multi-objective Optimization of Long-run Average and Total Rewards
Tim Quatmann,Joost-Pieter Katoen +1 more
TL;DR: This paper presents an efficient procedure for multi-objective model checking of long-run average reward and total reward objectives as well as their combination for Markov automata, a compositional model that captures both traditional Markov decision processes (MDPs) as a continuous-time variant thereof.
Verification of continuous space stochastic systems
TL;DR: This thesis deals with verification algorithms for inhomogeneous continuous time Markov chains (ICTMC), discrete time stochastic hybrid systems (DTSHS) and Markovian timed automata (MTA) to verify whether a DTSHS satisfies a given ω-regular property.
Journal ArticleDOI
Weighted programming: a programming paradigm for specifying mathematical models
TL;DR: It is argued that weighted programming as a paradigm can be used to specify mathematical models beyond probability distributions (as is done in probabilistic programming) and developed weakest-precondition- and weakest-liberal-pre condition-style calculi à la Dijkstra for reasoning about mathematical models specified by weighted programs.