搜索结果: 1-15 共查到“理论统计学 Markov Chain”相关记录23条 . 查询时间(0.235 秒)
Coupled coarse graining and Markov Chain Monte Carlo for lattice systems
Markov chain monte carlo random lattice model the short-range particles energy
2014/12/24
We propose an efficient Markov Chain Monte Carlo method for sampling equilibrium distributions for stochastic lattice models, capable of handling correctly long and short-range particle interactions. ...
Inference in Kingman's Coalescent with Particle Markov Chain Monte Carlo Method
Inference Kingman's Coalescent with Particle Markov Chain Monte Carlo Method
2013/6/13
We propose a new algorithm to do posterior sampling of Kingman's coalescent, based upon the Particle Markov Chain Monte Carlo methodology. Specifically, the algorithm is an instantiation of the Partic...
Discrepancy bounds for uniformly ergodic Markov chain quasi-Monte Carlo
Information visualization Formal Concept Analysis Galois sub-hierarchy
2013/4/27
In [Chen, D., Owen, Ann. Stat., 39, 673--701, 2011] Markov chain Monte Carlo (MCMC) was studied under the assumption that the driver sequence is a deterministic sequence rather than independent U(0,1)...
Adaptive Markov Chain Monte Carlo confidence intervals
Adaptive Markov Chain Monte Carlo confidence intervals
2012/11/22
In Adaptive Markov Chain Monte Carlo (AMCMC) simulation, classical estimators of asymptotic variances are inconsistent in general. In this work we establish that despite this inconsistency, confidence...
Geometric Allocation Approach for Transition Kernel of Markov Chain
Markov chain Transition kernel Geometric allocation
2011/7/7
We introduce a new geometric approach that constructs a transition kernel of Markov chain. Our method always minimizes the average rejection rate and even reduce it to zero in many relevant cases, whi...
Consistency of Markov chain quasi-Monte Carlo on continuous state spaces
Completely uniformly distributed coupling iterated function mappings Markov chain Monte Carlo
2011/6/17
The random numbers drivingMarkov chainMonte Carlo (MCMC)
simulation are usually modeled as independent U(0, 1) random variables.
Tribble [Markov chain Monte Carlo algorithms using completely
unifor...
Quantitative bounds for Markov chain convergence: Wasserstein and total variation distances
convergence rate coupling Gibbs sampler iterated random functions local
2011/3/24
We present a framework for obtaining explicit bounds on the rate of convergence to equilibrium of a Markov chain on a general state space, with respect to both total variation and Wasserstein distance...
A general purpose variance reduction technique for Markov chain Monte Carlo estimators based on the zero-variance principle introduced in the physics literature by Assaraf and Caffarel (1999, 2003), i...
Weak Convergence of Markov Chain Monte Carlo Methods and its Application to Regular Gibbs Sampler
Methodology (stat.ME) Statistics Theory (math.ST)
2010/12/17
In this paper, we introduce the notion of efficiency (consistency) and examine some asymptotic properties of Markov chain Monte Carlo methods. We apply these results to the Gibbs sampler for independe...
Asymptotic optimality of the cross-entropy method for Markov chain problems
Asymptotic optimality cross-entropy method Markov chain problems
2010/3/11
The correspondence between the cross-entropymethod and the zero-variance
approximation to simulate a rare event problem in Markov chains is shown. This
leads to a sufficient condition that the cross...
The reversible jump Markov chain Monte Carlo sampler (Green, 1995) provides a general
framework for Markov chain Monte Carlo (MCMC) simulation in which the dimension of the
parameter space can vary ...
In Bayesian inference, the posterior distribution for parameters 2 is given by (jy) /
(yj)(), where one's prior beliefs about the unknown parameters, as expressed through
the prior distrib...
A History of Markov Chain Monte Carlo——Subjective Recollections from Incomplete Data
History Markov Chain Monte Carlo——Subjective Recollections Incomplete Data
2010/4/30
In this note we attempt to trace the history and development of Markov chain
Monte Carlo (MCMC) from its early inception in the late 1940’s through its use today.
We see how the earlier stages of th...
Parameter Estimation in Continuous Time Markov Switching Models: A Semi-Continuous Markov Chain Monte Carlo Approach
Bayesian inference data augmentation hidden Markov model
2009/9/24
In this paper,we combine useful aspects of both approaches.On the one hand,we are inspired by the discretization, where filtering for the state process is possible,on the other hand,we
catch attracti...
On the convergence of weighted averages of random variables arising from a finite Markov chain
the convergence of weighted averages random variables a finite Markov chain
2009/9/23
On the convergence of weighted averages of random variables arising from a finite Markov chain。