We introduce a general class of distances (metrics) between Markov chains, which are based on linear behaviour. This class encompasses distances given topologically (such as the total variation distance or trace distance) as well as by temporal logics or automata. We investigate which of the distances can be approximated by observing the systems, i.e. by black-box testing or simulation, and we provide both negative and positive results
In this paper we synthesize our recent work on behavioral distances for probabilistic systems and pr...
We propose a distance between continuous-time Markov chains (CTMCs) and study the problem of computi...
In this paper we propose a complete axiomatization of the bisimilarity distance of Desharnais et al....
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 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...
Abstract. In this paper we propose two behavioral distances that sup-port approximate reasoning on S...
Abstract. In this paper we investigate distance functions on finite state Markov processes that meas...
In this paper we investigate distance functions on finite state Markov processes that measure the be...
A central questions in the field of probabilistic and Markovian systems is “when do two systems beha...
We prove results on the decidability and complexity of computing the total variation distance (equiv...
Semi-Markov chains (SMCs) are continuous-time probabilistic transition systems where the residence t...
In this paper we synthesize our recent work on behavioral distances for probabilistic systems and pr...
We propose a distance between continuous-time Markov chains (CTMCs) and study the problem of computi...
In this paper we propose a complete axiomatization of the bisimilarity distance of Desharnais et al....
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 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...
Abstract. In this paper we propose two behavioral distances that sup-port approximate reasoning on S...
Abstract. In this paper we investigate distance functions on finite state Markov processes that meas...
In this paper we investigate distance functions on finite state Markov processes that measure the be...
A central questions in the field of probabilistic and Markovian systems is “when do two systems beha...
We prove results on the decidability and complexity of computing the total variation distance (equiv...
Semi-Markov chains (SMCs) are continuous-time probabilistic transition systems where the residence t...
In this paper we synthesize our recent work on behavioral distances for probabilistic systems and pr...
We propose a distance between continuous-time Markov chains (CTMCs) and study the problem of computi...
In this paper we propose a complete axiomatization of the bisimilarity distance of Desharnais et al....