Abstract We introduce the exit time finite state projection (ETFSP) scheme, a truncation-based method that yields approximations to the exit distribution and occupation measure associated with the time of exit from a domain (i.e., the time of first passage to the complement of the domain) of time-homogeneous continuous-time Markov chains. We prove that (i) the computed approximations bound the measures from below; (ii) the total variation distances between the approximations and the measures decrease monotonically as states are added to the truncation; and (iii) the scheme converges, in the sense that, as the truncation tends to the entire state space, the total variation distances tend to zero. Furthermore, we give a computable bound on t...
Cataloged from PDF version of article.We propose a bounding technique for the equilibrium probabilit...
The goal of this work is to formally abstract a Markov process evolving in discrete time over a gene...
We explore formal approximation techniques for Markov chains based on state–space reduction t...
Abstract We introduce the exit time finite state projection (ETFSP) scheme, a truncation-based meth...
We introduce the exit time finite state projection (ETFSP) scheme, a truncation- based method that y...
This dissertation presents a theoretical study of arbitrary discretizations of general nonequilibriu...
Abstract Computing the stationary distributions of a continuous-time Markov chain (CTMC) involves s...
International audienceWe propose new bounds and approximations for the transition probabilities of a...
Many gene regulatory networks are modeled at the mesoscopic scale, where chemical pop-ulations chang...
For dealing numerically with the infinite-state-space Markov chains, a truncation of the state space...
Computing the stationary distributions of a continuous-time Markov chain involves solving a set of l...
This thesis is a monograph on Markov chains and deterministic approximation schemes that enable the...
We consider an additive functional driven by a time-inhomogeneous Markov chain with a finite state s...
Based upon the Grassman, Taksar and Heyman algorithm [1] and the equivalent Sheskin State Reduction ...
We consider a continuous-time Markov process on a large continuous or discrete state space. The proc...
Cataloged from PDF version of article.We propose a bounding technique for the equilibrium probabilit...
The goal of this work is to formally abstract a Markov process evolving in discrete time over a gene...
We explore formal approximation techniques for Markov chains based on state–space reduction t...
Abstract We introduce the exit time finite state projection (ETFSP) scheme, a truncation-based meth...
We introduce the exit time finite state projection (ETFSP) scheme, a truncation- based method that y...
This dissertation presents a theoretical study of arbitrary discretizations of general nonequilibriu...
Abstract Computing the stationary distributions of a continuous-time Markov chain (CTMC) involves s...
International audienceWe propose new bounds and approximations for the transition probabilities of a...
Many gene regulatory networks are modeled at the mesoscopic scale, where chemical pop-ulations chang...
For dealing numerically with the infinite-state-space Markov chains, a truncation of the state space...
Computing the stationary distributions of a continuous-time Markov chain involves solving a set of l...
This thesis is a monograph on Markov chains and deterministic approximation schemes that enable the...
We consider an additive functional driven by a time-inhomogeneous Markov chain with a finite state s...
Based upon the Grassman, Taksar and Heyman algorithm [1] and the equivalent Sheskin State Reduction ...
We consider a continuous-time Markov process on a large continuous or discrete state space. The proc...
Cataloged from PDF version of article.We propose a bounding technique for the equilibrium probabilit...
The goal of this work is to formally abstract a Markov process evolving in discrete time over a gene...
We explore formal approximation techniques for Markov chains based on state–space reduction t...