AbstractThe discrete autoregressive and minification stationary time series models discussed by Little-john (1992a) are generalized to model marginal distributions which have perturbations at the origin. The reversibility theorem relating these processes with geometric marginal distribution is extended to the case where the marginal distribution has geometric tail
In this paper we introduce a binary autoregressive model. In contrast to the typical autoregression ...
summary:An iterative procedure for computation of stationary density of autoregressive processes is ...
Abstract. We introduce a class of stationary processes characterized by the behaviour of their infin...
AbstractThe discrete autoregressive and minification stationary time series models discussed by Litt...
AbstractThe present paper deals with reversibility of autoregressive processes of first order, namel...
In this paper we show that particular Gibbs sampler Markov processes can be modified to an autoregre...
A Markov decision problem is called reversible if the stationary controlled Markov chain is reversib...
The explicit control of the convergence of properly normalized sums of random variables, as well as ...
Given a target distribution $\pi$ and an arbitrary Markov infinitesimal generator $L$ on a finite st...
The present paper deals with reversibility of autoregressive processes of first order, namely AR(1)....
We present a stochastic model which yields a stationary Markov process whose invariant distribution ...
Random processes can be used to describe the evolution of a real systems over time. Discrete-time Ma...
This dissertation describes the research that we have done concerning reversible Markov chains. We f...
AbstractMarkov chains on an infinite product space are considered whose transition kernel is of the ...
Time reversibility plays an important role in the analysis of continuous and discrete time Markov ch...
In this paper we introduce a binary autoregressive model. In contrast to the typical autoregression ...
summary:An iterative procedure for computation of stationary density of autoregressive processes is ...
Abstract. We introduce a class of stationary processes characterized by the behaviour of their infin...
AbstractThe discrete autoregressive and minification stationary time series models discussed by Litt...
AbstractThe present paper deals with reversibility of autoregressive processes of first order, namel...
In this paper we show that particular Gibbs sampler Markov processes can be modified to an autoregre...
A Markov decision problem is called reversible if the stationary controlled Markov chain is reversib...
The explicit control of the convergence of properly normalized sums of random variables, as well as ...
Given a target distribution $\pi$ and an arbitrary Markov infinitesimal generator $L$ on a finite st...
The present paper deals with reversibility of autoregressive processes of first order, namely AR(1)....
We present a stochastic model which yields a stationary Markov process whose invariant distribution ...
Random processes can be used to describe the evolution of a real systems over time. Discrete-time Ma...
This dissertation describes the research that we have done concerning reversible Markov chains. We f...
AbstractMarkov chains on an infinite product space are considered whose transition kernel is of the ...
Time reversibility plays an important role in the analysis of continuous and discrete time Markov ch...
In this paper we introduce a binary autoregressive model. In contrast to the typical autoregression ...
summary:An iterative procedure for computation of stationary density of autoregressive processes is ...
Abstract. We introduce a class of stationary processes characterized by the behaviour of their infin...