Publication | Closed Access
Weak convergence of conditioned processes on a countable state space
67
Citations
11
References
1995
Year
EngineeringGibbs MeasureIntegrable ProbabilityConditional ProcessStochastic ProcessesMarkov KernelStochastic Dynamical SystemWeak Convergence FailsProbability TheoryWeak ConvergenceFunctional AnalysisMarkov Decision Process
We consider the problem of conditioning a continuous-time Markov chain (on a countably infinite state space) not to hit an absorbing barrier before time T ; and the weak convergence of this conditional process as T → ∞. We prove a characterization of convergence in terms of the distribution of the process at some arbitrary positive time, t , introduce a decay parameter for the time to absorption, give an example where weak convergence fails, and give sufficient conditions for weak convergence in terms of the existence of a quasi-stationary limit, and a recurrence property of the original process.
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