Add to ORKGContinuous-time Markov chains are a popular tool for stochastic modelling in a great variety of fields, ranging from population genetics to communication networks. The mathematical theory of such processes is well understood and provides powerful results for the handling of applied problems. Recently suc
There are very many processes in the natural and social sciences which can be represented as a set o...
We consider continuous-time Markov chains on integers which allow transitions to adjacent states onl...
Switching processes, invented by the author in 1977, is the main tool used in the investigation of t...
Existing models of herding suffer from the drawback that conventional measures assume it is constant...
Many large-scale stochastic systems, such as telecommunications networks, can be modelled using a co...
Markov switching models are a family of models that introduces time variation in the parameters in t...
The consideration of quantitative data is often required to perform research in both the physical an...
Regime-switching models, in particular Hidden Markov Models (HMMs) where the switching is driven by ...
We study Markovian models for population processes in continuous time, addressing questions concerni...
This book presents a concise introduction to piecewise deterministic Markov processes (PDMPs), with ...
This chapter introduces some basic mathematical tools that are widely used in the performance analys...
This self-contained text develops a Markov chain approach that makes the rigorous analysis of a clas...
Markov chains are a fundamental class of stochastic processes. They are widely used to solve problem...
The paper is devoted to the study of the asymptotic behaviour of Moran process in random environment...
We consider continuous-time Markov chains on integers which allow transitions to adjacent states onl...
There are very many processes in the natural and social sciences which can be represented as a set o...
We consider continuous-time Markov chains on integers which allow transitions to adjacent states onl...
Switching processes, invented by the author in 1977, is the main tool used in the investigation of t...
Existing models of herding suffer from the drawback that conventional measures assume it is constant...
Many large-scale stochastic systems, such as telecommunications networks, can be modelled using a co...
Markov switching models are a family of models that introduces time variation in the parameters in t...
The consideration of quantitative data is often required to perform research in both the physical an...
Regime-switching models, in particular Hidden Markov Models (HMMs) where the switching is driven by ...
We study Markovian models for population processes in continuous time, addressing questions concerni...
This book presents a concise introduction to piecewise deterministic Markov processes (PDMPs), with ...
This chapter introduces some basic mathematical tools that are widely used in the performance analys...
This self-contained text develops a Markov chain approach that makes the rigorous analysis of a clas...
Markov chains are a fundamental class of stochastic processes. They are widely used to solve problem...
The paper is devoted to the study of the asymptotic behaviour of Moran process in random environment...
We consider continuous-time Markov chains on integers which allow transitions to adjacent states onl...
There are very many processes in the natural and social sciences which can be represented as a set o...
We consider continuous-time Markov chains on integers which allow transitions to adjacent states onl...
Switching processes, invented by the author in 1977, is the main tool used in the investigation of t...