搜索结果: 1-12 共查到“理论统计学 Markov chain Monte Carlo”相关记录12条 . 查询时间(0.121 秒)
Coupled coarse graining and Markov Chain Monte Carlo for lattice systems
Markov chain monte carlo random lattice model the short-range particles energy
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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
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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...
Adaptive Markov Chain Monte Carlo confidence intervals
Adaptive Markov Chain Monte Carlo confidence intervals
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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...
Zero Variance Markov Chain Monte Carlo for Bayesian Estimators
Computation (stat.CO)
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2010/12/17
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)
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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...
Reversible jump Markov chain Monte Carlo
Reversible Jump Markov chain Monte Carlo
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2010/3/9
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 ...
Likelihood-free Markov chain Monte Carlo
Likelihood-free Markov chain Monte Carlo
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2010/3/9
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
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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
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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...
EM versus Markov chain Monte Carlo for estimation of hidden Markov models: a computational perspective
hidden Markov model incomplete data missing data EM trans-dimensional Monte Carlo computational statistics
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2009/9/22
Hidden Markov models (HMMs) and related models have become stan-
dard in statistics during the last 15C2 years, with applications in diverse areas
like speech and other statistical signal processing...
How to Combine Fast Heuristic Markov Chain Monte Carlo with Slow Exact Sampling
Confidence interval Exact sampling Markov Chain Monte Carlo
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2009/5/4
Given a probability law $pi$ on a set S and a function $g : S rightarrow R$, suppose one wants to estimate the mean $bar{g} = int g dpi$. The Markov Chain Monte Carlo method consists of inventing and ...
Markov Chain Monte Carlo：Can We Trust the Third Significant Figure?
Convergence diagnostic Markov chain MonteCarlo standard errors
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2010/4/27
Current reporting of results based on Markov chain Monte
Carlo computations could be improved. In particular, a measure of the
accuracy of the resulting estimates is rarely reported. Thus we have
l...