搜索结果: 1-15 共查到“统计学 Markov chains”相关记录26条 . 查询时间(0.249 秒)
Nested hidden Markov chains for modeling dynamic unobserved heterogeneity in multilevel longitudinal data
composite likelihood EM algorithm latent Markov model pairwise likelihood
2012/9/17
In the context of multilevel longitudinal data, where sample units are collected in clusters, an important aspect that should be accounted for is the unobserved heterogeneity between sample units and ...
We determine an explicit Gr¨obner basis, consisting of linear forms and determi-nantal quadrics, for the prime ideal of Raftery’s mixture transition distribution model for Markov chains. When the stat...
A central limit theorem for adaptive and interacting Markov chains
MCMC interacting MCMC Limit theorems
2011/7/19
Adaptive and interacting Markov Chains Monte Carlo (MCMC) algorithms are a novel class of non-Markovian algorithms aimed at improving the simulation efficiency for complicated target distributions.
An EM Algorithm for Continuous-time Bivariate Markov Chains
Parameter estimation EM algorithm Continuous-time bivariate Markov chain
2011/7/19
We study properties and parameter estimation of finite-state homogeneous continuous-time bivariate Markov chains.
Bayesian analysis of variable-order, reversible Markov chains
Reversibility reinforced random walks variable-order Markov chains Bayesian analysis conjugate priors
2011/6/17
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
...
Regenerative block empirical likelihood for Markov chains
Nummelin splitting technique time series Empirical Likelihood
2011/3/21
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 ...
Regenerative block empirical likelihood for Markov chains
Nummelin splitting technique time series Empirical Likelihood MSC codes: 62G05 62F35 62F40
2011/3/23
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 ...
CLTs and asymptotic variance of time-sampled Markov chains
time-sampled Markov chains Barker’s algo-rithm Metropolis algorithm Central Limit Theorem asymptotic variance variance bounding Markov chains MCMC estimation
2011/3/25
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...
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 ...
Geometric ergodicity for families of homogeneous Markov chains
Homogeneous Markov chain Geometric ergodicity Couplingrenewal processes Lyapunov function Renewal theory
2010/3/11
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...
Invariant measures and Markov chains with random transition probabilities
Invariant measures Markov chains random transition probabilities
2009/9/22
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
...
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...
Uniform moderate deviations of functional empirical processes of Markov chains
Moderak deviations Markov chains geometric ergodicity regeneration split chain method
2009/9/22
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...
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
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...