搜索结果: 46-60 共查到“统计学 Sampling”相关记录78条 . 查询时间(0.101 秒)
Elliptical Slice Sampling
Elliptical Slice Sampling probabilistic models Gaussian distribution
2010/3/9
Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm f...
Comparison of some statistical experiments associated with sampling plans
Comparison some statistical experiments sampling plans
2009/9/24
Comparison of some statistical experiments associated with sampling plans。
Asymptotic theory of linear statistics in sampling proportional to size without replacement
Asymptotic theory of linear statistics sampling proportional size without replacement
2009/9/22
Consider an ordered sample that is selected from
a finite population successively without replacement and with probability
proportional to some measure of size. In this paper, we study the
asymptot...
Reconsidering Neyman on experimentation and sampling: controversies and fundamental contributions
Reconsidering Neyman experimentation sampling controversies fundamental contributions
2009/9/22
What seems especially fascinating to us, having taken the
occasion of the 100th anniversary of Neyman's birth to reread many of the
early papers and writings in the areas of sampling and experimenta...
Model-Based Inferences from Adaptive Cluster Sampling
Informative sampling MCMC spatial sampling zero-inated count data
2009/9/22
Adaptive cluster sampling is useful for exploring populations of rare
plant and animal species which cluster together because it allows sampling eort
to be concentrated in areas where observed value...
Importance Re-sampling MCMC for Cross-Validation in Inverse Problems
Cross-validation Inverse Importance Re-sampling Model fit Re-use
2009/9/22
This paper presents a methodology for cross-validation in the context of Bayesian
modelling of situations we loosely refer to as iverse problems It is motivated by
an example from palaeoclimatology ...
Nested Sampling for General Bayesian Computation
Bayesian computation evidence marginal likelihood algorithm nest annealing phase change model selection
2009/9/21
Nested sampling estimates directly how the likelihood function relates
to prior mass. The evidence (alternatively the marginal likelihood, marginal den-
sity of the data, or the prior predictive) is...
A note on impotance sampling simulation for germ-grain model
Germ-gain model Poisson process change of measure likelihood process stopping set
2009/9/21
In this paper we demonstrate how to use the importance
sampling method to simulate rare wmts in a germ-grain model. We
analyze conditions under which two gerrn-grain models are mutually
absolutely ...
Improvement on Estimating Current Population Ratio in Successive Sampling
Estimating Current Population Ratio Successive Sampling
2009/9/17
Improvement on Estimating Current Population Ratio in Successive Sampling。
On a πps Scheme of Sampling of Two Units。
Bootstrap of means under stratified sampling
Cluster sampling bootstrap second-order asymptotic
2009/9/16
In a two-stage cluster sampling procedure, $n$ random populations are drawn independently from independent populations and a sub-sample of observations is taken in each of them. The estimator of the g...
Generalised linear mixed model analysis via sequential Monte Carlo sampling
generalised additive models longitudinal data analysis nonparametric regression sequential Monte Carlo sampler
2009/9/16
We present a sequential Monte Carlo algorithm for the Bayesian analysis of generalised linear mixed models (GLMMs). These models support a variety of interesting regression-type analyses, but performi...
Case-deletion importance sampling estimators: Central limit theorems and related results
Infinite Variance Influence Leverage Marginal Residual Sum of Squares Markov Chain Monte Carlo Model Averaging Moment Index Tail Behavior
2009/9/16
Case-deleted analysis is a popular method for evaluating the influence of a subset of cases on inference. The use of Monte Carlo estimation strategies in complicated Bayesian settings leads naturally ...
Construction of weakly CUD sequences for MCMC sampling
completely uniformly distributed Gibbs sampler equidistribution probit quasi-Monte Carlo
2009/9/16
In Markov chain Monte Carlo (MCMC) sampling considerable thought goes into constructing random transitions. But those transitions are almost always driven by a simulated IID sequence. Recently it has ...
How to Combine Fast Heuristic Markov Chain Monte Carlo with Slow Exact Sampling
Confidence interval Exact sampling Markov Chain Monte Carlo
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 ...