Add to ORKGThis is a formalization of probabilistic models in Isabelle/HOL. It builds on Isabelle’s probability theory. The available models are currently Discrete-Time Markov Chains and a extensions of them with rewards. As application of these models we formalize probabilistic model checking of pCTL formulas, analysis of IPv4 address allocation in Ze-roConf and a analysis of the anonymity of the Crowds protocol. The formalization of rewarded DTMCs and pCTL model checkin
This tutorial provides an introduction to probabilistic model checking, a technique for automaticall...
Probabilistic model checking is a formal verification technique for systems that exhibit stochastic ...
Kozen introduced probabilistic propositional dynamic logic (PPDL) in 1985 as a compositional framewo...
This paper presents a model-checking approach for analyzing discrete-time Markov reward models. For ...
Probabilistic model checkers like PRISM only check probabilistic systems of a fixed size. To guar-an...
The branching-time temporal logic PCTL* has been introduced to specify quantitative properties over ...
Abstract. The branching-time temporal logic PCTL ¤ has been intro-duced to specify quantitative prop...
AbstractWe introduce p-Automata, which are automata that accept languages of Markov chains, by adapt...
Abstract. Numerous models of probabilistic systems are studied in the litera-ture. Coalgebra has bee...
Markov decision processes (MDPs) are natural models of computation in a wide range of applications. ...
Abstract Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clar...
Probabilistic (or quantitative) verification is a branch of formal methods dealing with stochastic m...
Interval-valued discrete time Markov chains analysis Description Probabilistic model checking is a w...
Probabilistic analysis is a tool of fundamental importance to virtually all scientists and engineers...
International audienceWe propose a way of presenting and computing a counterexample in probabilistic...
This tutorial provides an introduction to probabilistic model checking, a technique for automaticall...
Probabilistic model checking is a formal verification technique for systems that exhibit stochastic ...
Kozen introduced probabilistic propositional dynamic logic (PPDL) in 1985 as a compositional framewo...
This paper presents a model-checking approach for analyzing discrete-time Markov reward models. For ...
Probabilistic model checkers like PRISM only check probabilistic systems of a fixed size. To guar-an...
The branching-time temporal logic PCTL* has been introduced to specify quantitative properties over ...
Abstract. The branching-time temporal logic PCTL ¤ has been intro-duced to specify quantitative prop...
AbstractWe introduce p-Automata, which are automata that accept languages of Markov chains, by adapt...
Abstract. Numerous models of probabilistic systems are studied in the litera-ture. Coalgebra has bee...
Markov decision processes (MDPs) are natural models of computation in a wide range of applications. ...
Abstract Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clar...
Probabilistic (or quantitative) verification is a branch of formal methods dealing with stochastic m...
Interval-valued discrete time Markov chains analysis Description Probabilistic model checking is a w...
Probabilistic analysis is a tool of fundamental importance to virtually all scientists and engineers...
International audienceWe propose a way of presenting and computing a counterexample in probabilistic...
This tutorial provides an introduction to probabilistic model checking, a technique for automaticall...
Probabilistic model checking is a formal verification technique for systems that exhibit stochastic ...
Kozen introduced probabilistic propositional dynamic logic (PPDL) in 1985 as a compositional framewo...