AbstractWe consider convergence of Markov chains with uncertain parameters, known as imprecise Markov chains, which contain an absorbing state. We prove that under conditioning on non-absorption the imprecise conditional probabilities converge independently of the initial imprecise probability distribution if some regularity conditions are assumed. This is a generalisation of a known result from the classical theory of Markov chains by Darroch and Seneta [6]
AbstractThe parameters of Markov chain models are often not known precisely. Instead of ignoring thi...
We prove a game-theoretic version of the strong law of large numbers for submartingale differences, ...
An imprecise Markov chain is defined by a closed convex set of transition matrices instead of a uniq...
AbstractWe consider convergence of Markov chains with uncertain parameters, known as imprecise Marko...
Many Markov chains with a single absorbing state have a unique limiting conditional distribution (LC...
Recently, Elmes et al. (see [2]) proposed a definition of a quasistationary distribution to accommod...
Many Markov chains with a single absorbing state have a unique limiting conditional distribution (LC...
When the initial and transition probabilities of a finite Markov chain in discrete time are not well...
Recently, Elmes, Pollett and Walker [2] proposed a definition of a quasistationary distribution to a...
We study the average behaviour of imprecise Markov chains; a generalised type of Markov chain where ...
In a recent paper [4] it was shown that, for an absorbing Markov chain where absorption is not guara...
We consider the problem of characterising expected hitting times and hitting probabilities for impre...
We consider the problem of conditioning a continuous-time Markov chain (on a countably infinite stat...
A Hidden Markov Model generates two basic stochastic processes, a Markov chain, which is hidden, and...
We consider a discrete-time Markov chain on the non-negative integers with drift to infinity and stu...
AbstractThe parameters of Markov chain models are often not known precisely. Instead of ignoring thi...
We prove a game-theoretic version of the strong law of large numbers for submartingale differences, ...
An imprecise Markov chain is defined by a closed convex set of transition matrices instead of a uniq...
AbstractWe consider convergence of Markov chains with uncertain parameters, known as imprecise Marko...
Many Markov chains with a single absorbing state have a unique limiting conditional distribution (LC...
Recently, Elmes et al. (see [2]) proposed a definition of a quasistationary distribution to accommod...
Many Markov chains with a single absorbing state have a unique limiting conditional distribution (LC...
When the initial and transition probabilities of a finite Markov chain in discrete time are not well...
Recently, Elmes, Pollett and Walker [2] proposed a definition of a quasistationary distribution to a...
We study the average behaviour of imprecise Markov chains; a generalised type of Markov chain where ...
In a recent paper [4] it was shown that, for an absorbing Markov chain where absorption is not guara...
We consider the problem of characterising expected hitting times and hitting probabilities for impre...
We consider the problem of conditioning a continuous-time Markov chain (on a countably infinite stat...
A Hidden Markov Model generates two basic stochastic processes, a Markov chain, which is hidden, and...
We consider a discrete-time Markov chain on the non-negative integers with drift to infinity and stu...
AbstractThe parameters of Markov chain models are often not known precisely. Instead of ignoring thi...
We prove a game-theoretic version of the strong law of large numbers for submartingale differences, ...
An imprecise Markov chain is defined by a closed convex set of transition matrices instead of a uniq...
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