Implementing random scan Gibbs samplers
Copyright policy )Implementing random scan Gibbs samplers
A Gibbs sampler is considered for the simulation of a multivariate distribution \(X\). It is proposed to change the probabilities of coordinate selection at each step taking into account the information from previously generated data. If the sampler is used to estimate a functional moment \({\mathbf E} h(X)\), then the variance of the estimator can be used as a criterion of optimality. The authors use a Gaussian approximation to derive a feasible criterion. Properties of the obtained adaptive sampler are studied via simulations. It is applied to the analysis of real medical data.
- Rice University United States
- University of California, Davis United States
- Lawrence Livermore National Laboratory United States
- San Diego State University United States
- Lawrence Berkeley National Laboratory United States
Analysis of variance and covariance (ANOVA), adaptive sampler, Monte Carlo methods, Gaussian approximation, simulation, Markov chain Monte Carlo method, multivariate distribution, Gibbs sampler, Sampling theory, sample surveys, Numerical analysis or methods applied to Markov chains, Computational methods for problems pertaining to statistics
Analysis of variance and covariance (ANOVA), adaptive sampler, Monte Carlo methods, Gaussian approximation, simulation, Markov chain Monte Carlo method, multivariate distribution, Gibbs sampler, Sampling theory, sample surveys, Numerical analysis or methods applied to Markov chains, Computational methods for problems pertaining to statistics
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).14 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!