We consider a continuous time Markov process (X, L), where X jumps between a finite number of states and L is a piecewise linear process with state space Rd. The process L represents an “inert drift ” or “reinforcement. ” We find sufficient and necessary conditions for the process (X, L) to have a stationary distribution of the product form, such that the marginal distribution of L is Gaussian. We present a number of conjectures for processes with a similar structure but with continuous state spaces.
Absrract. Given a finite state Markov process {X,), t 3 0, a global "driving noise " proce...
This paper contains a survey of results related to quasi-stationary distributions, which arise in th...
We investigate a piecewise-deterministic Markov process, evolving on a Polish metric space, whose de...
In this paper we address the problem of efficiently deriving the steady-state distribution for a con...
Abstract: We consider continuous-time Markov chains representing queueing systems in random environm...
International audienceWe consider discrete time Markov chains in competition over a set of resources...
A discrete-time Markov process with a bounded continuous state space is considered. We show that the...
This paper concerns a Markovian piecewise linear process, based on a continuous-time Markov chain wi...
The aim of this minicourse is to provide a number of tools that allow one to de-termine at which spe...
v4: more details and a fix for the constructive proof of the bracket condition.We study a class of P...
International audienceWe consider Stochastic Automata Networks (SAN) in continuous time and we prove...
This paper contains a survey of results related to quasi-stationary distributions, which arise in th...
We study a class of Piecewise Deterministic Markov Processes with state space Rd × E where E is a fi...
AbstractConsider a continuous time Markov chain with stationary transition probabilities. A function...
Abstract Computing the stationary distributions of a continuous-time Markov chain (CTMC) involves s...
Absrract. Given a finite state Markov process {X,), t 3 0, a global "driving noise " proce...
This paper contains a survey of results related to quasi-stationary distributions, which arise in th...
We investigate a piecewise-deterministic Markov process, evolving on a Polish metric space, whose de...
In this paper we address the problem of efficiently deriving the steady-state distribution for a con...
Abstract: We consider continuous-time Markov chains representing queueing systems in random environm...
International audienceWe consider discrete time Markov chains in competition over a set of resources...
A discrete-time Markov process with a bounded continuous state space is considered. We show that the...
This paper concerns a Markovian piecewise linear process, based on a continuous-time Markov chain wi...
The aim of this minicourse is to provide a number of tools that allow one to de-termine at which spe...
v4: more details and a fix for the constructive proof of the bracket condition.We study a class of P...
International audienceWe consider Stochastic Automata Networks (SAN) in continuous time and we prove...
This paper contains a survey of results related to quasi-stationary distributions, which arise in th...
We study a class of Piecewise Deterministic Markov Processes with state space Rd × E where E is a fi...
AbstractConsider a continuous time Markov chain with stationary transition probabilities. A function...
Abstract Computing the stationary distributions of a continuous-time Markov chain (CTMC) involves s...
Absrract. Given a finite state Markov process {X,), t 3 0, a global "driving noise " proce...
This paper contains a survey of results related to quasi-stationary distributions, which arise in th...
We investigate a piecewise-deterministic Markov process, evolving on a Polish metric space, whose de...