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We study properties and parameter estimation of finite-state homogeneous continuous-time bivariate Markov chains.
We define a conjugate prior for the reversible Markov chain of order r. The prior arises from a partially exchangeable reinforced random walk, in the same way that the Beta distribution arises from ...
Empirical likelihood is a powerful semi-parametric method increasingly investigated in the literature. However, most authors essentially focus on an i.i.d. setting. In the case of dependent data, the ...
For a Markov transition kernel $P$ and a probability distribution $ \mu$ on nonnegative integers, a time-sampled Markov chain evolves according to the transition kernel $P_{\mu} = \sum_k \mu(k)P^k.$ I...
Damage spreading and coupling in Markov chains     Damage spreading  coupling  Markov chains       font style='font-size:12px;'> 2010/10/19
In this paper, we relate the coupling of Markov chains, at the basis of perfect sampling methods, with damage spreading, which captures the chaotic nature of stochastic dynamics. For two-dimensional ...
In this paper we find nonasymptotic exponential upper bounds for the deviation in the ergodic theorem for families of homogeneous Markov processes. We find some sufficient conditions for geometric e...
In this paper, suTTicient conditions for the existence of (a-finite) invariant measures for a class of Markov chains with random transition probabilities are given. A special class of Markov chains ...
Local invariance principle for Markov chains      Local invariance principle  Markov chains        font style='font-size:12px;'> 2009/9/22
We consider stationary homogeneous Markov chains and the polygonal processes defined by a usual way using such chains. There are many results about invariance principles of those processes. In this...
We obtain uniform (in time) moderate deviations for the functional empirical process ofa general state space Markov chain under the geometric ergodicity assumption, and a regularity condition for t...
General state space Markov chains and MCMC algorithms     Markov chains  MCMC algorithms       font style='font-size:12px;'> 2009/5/18
This paper surveys various results about Markov chains on general (non-countable) state spaces. It begins with an introduction to Markov chain Monte Carlo (MCMC) algorithms, which provide the motivati...
Differential equation approximations for Markov chains     Differential equation  Markov chains       font style='font-size:12px;'> 2009/5/18
We formulate some simple conditions under which a Markov chain may be approximated by the solution to a differential equation, with quantifiable error probabilities. The role of a choice of coordinate...
Geometric Ergodicity and Hybrid Markov Chains     Ergodicity  Markov Chain  Monte Carlo       font style='font-size:12px;'> 2009/5/8
Various notions of geometric ergodicity for Markov chains on general state spaces exist. In this paper, we review certain relations and implications among them. We then apply these results to a collec...
Transition Probability Estimates for Reversible Markov Chains     Random walks  Markov chains  fractals  dimensions       font style='font-size:12px;'> 2009/5/4
This paper provides transition probability estimates of transient reversible Markov chains. The key condition of the result is the spatial symmetry and polynomial decay of the Green's function of the ...
We state and prove a simple quantitative bound on the total variation distance after k iterations between two Markov chains with different initial distributions but identical transition probabilities....
We consider a ψ-irreducible, discrete-time Markov chain on a general state space with transition kernel P. Under suitable conditions on the chain, kernels can be treated as bounded linear operators...

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