Semi-Markov chains (SMCs) are continuous-time probabilistic transition systems where the residence time on states is governed by generic distributions on the positive real line. This paper shows the tight relation between the total variation distance on SMCs and their model checking problem over linear real-time specifications. Specifically, we prove that the total variation between two SMCs coincides with the maximal difference w.r.t. the likelihood of satisfying arbitrary MTL formulas or ω-languages recognized by timed automata. Computing this distance (i.e., solving its threshold problem) is NPhard and its decidability is an open problem. Nevertheless, we propose an algorithm for approximating it with arbitrary precision
In this paper, we consider the behavioral pseudometrics for probabilistic systems, which are a quant...
Recent investigations have shown that the automated verification of continuous-time Markov chains (C...
In this paper, we consider the behavioral pseu-dometrics for probabilistic systems. The model we are...
Abstract. Semi-Markov chains (SMCs) are continuous-time probabilis-tic transition systems where the ...
Semi-Markov chains (SMCs) are continuous-time probabilistic transition systems where the residence t...
Labelled Markov chains (LMCs) are widely used in probabilistic verification, speech recognition, com...
We prove results on the decidability and complexity of computing the total variation distance (equiv...
Abstract. In this paper we propose two behavioral distances that sup-port approximate reasoning on S...
We introduce a general class of distances (metrics) between Markov chains, which are based on linear...
We introduce a general class of distances (metrics) between Markov chains, which are based on linear...
We prove results on the decidability and complexity of computing the total variation distance (equiv...
We introduce a general class of distances (metrics) between Markov chains, which are based on linear...
Abstract. We study the strong and strutter trace distances on Markov chains (MCs). Our interest in t...
We study two well-known linear-time metrics on Markov chains (MCs), namely, the strong and strutter ...
We study the strong and strutter trace distances on Markov chains (MCs). Our interest in these metri...
In this paper, we consider the behavioral pseudometrics for probabilistic systems, which are a quant...
Recent investigations have shown that the automated verification of continuous-time Markov chains (C...
In this paper, we consider the behavioral pseu-dometrics for probabilistic systems. The model we are...
Abstract. Semi-Markov chains (SMCs) are continuous-time probabilis-tic transition systems where the ...
Semi-Markov chains (SMCs) are continuous-time probabilistic transition systems where the residence t...
Labelled Markov chains (LMCs) are widely used in probabilistic verification, speech recognition, com...
We prove results on the decidability and complexity of computing the total variation distance (equiv...
Abstract. In this paper we propose two behavioral distances that sup-port approximate reasoning on S...
We introduce a general class of distances (metrics) between Markov chains, which are based on linear...
We introduce a general class of distances (metrics) between Markov chains, which are based on linear...
We prove results on the decidability and complexity of computing the total variation distance (equiv...
We introduce a general class of distances (metrics) between Markov chains, which are based on linear...
Abstract. We study the strong and strutter trace distances on Markov chains (MCs). Our interest in t...
We study two well-known linear-time metrics on Markov chains (MCs), namely, the strong and strutter ...
We study the strong and strutter trace distances on Markov chains (MCs). Our interest in these metri...
In this paper, we consider the behavioral pseudometrics for probabilistic systems, which are a quant...
Recent investigations have shown that the automated verification of continuous-time Markov chains (C...
In this paper, we consider the behavioral pseu-dometrics for probabilistic systems. The model we are...