搜索结果: 31-45 共查到“统计学 Sampling”相关记录78条 . 查询时间(0.377 秒)
Approximate inference via variational sampling
variational sampling limit theorem probability distribution
2011/6/16
We propose a new method to approximately integrate a function with respect
to a given probability distribution when an exact computation is intractable. The
method is called \variational sampling" a...
Metamodel-based importance sampling for structural reliability analysis
reliability analysis importance sampling metamodeling error kriging random fields active learning rare events
2011/6/16
Structural reliability methods aim at computing the probability of failure of systems with
respect to some prescribed performance functions. In modern engineering such functions
usually resort to ru...
Multivariate stratified sampling by stochastic multiobjective optimisation
Multivariate stratified random sampling multiobjective E-model
2011/7/5
This work considers the allocation problem for multivariate stratified random sampling as a problem of integer non-linear stochastic multiobjective mathematical programming.
Breadth First Search (BFS) is a widely used approach for sampling large unknown Internet topologies. Its main advantage over random walks and other exploration techniques is that a BFS sample is a pla...
Restricted Collapsed Draw: Accurate Sampling for Hierarchical Chinese Restaurant Process Hidden Markov Models
Restricted Collapsed Draw Accurate Sampling Hierarchical Chinese
2011/7/5
We propose a restricted collapsed draw (RCD) sampler, a general Markov chain Monte Carlo sampler of simultaneous draws from a hierarchical Chinese restaurant process (HCRP) with restriction.
On statistical uncertainty in nested sampling
uncertainty Nested sampling Bayesian analysis
2011/3/25
Nested sampling has emerged as a valuable tool for Bayesian analysis, in particular for determining the Bayesian evidence. The method is based on a specific type of random sampling of the likelihood f...
Generalized Species Sampling Priors with Latent Beta reinforcements
Statistics Theory (math.ST) Learning (cs.LG) Methodology (stat.ME)
2010/12/17
Many popular Bayesian Nonparametric priors can be characterized in terms of exchangeable species sampling sequences. One example is the Dirichlet Process prior, that has been increasingly used for mod...
Product-limit estimators of the gap time distribution of a renewal process under different sampling patterns
Kaplan-Meier estimator Cox-Vardi estimator Laslett's line segment problem nonparametric maximum likelihood Markov process
2010/3/11
Nonparametric estimation of the gap time distribution in a simple re-
newal process may be considered a problem in survival analysis under
particular sampling frames corresponding to how the renewal...
Quantile estimation with adaptive importance sampling
Quantile estimation law of iterated logarithm adaptive im-portance sampling stochastic approximation Robbins–Monro
2010/3/11
We introduce new quantile estimators with adaptive importance
sampling. The adaptive estimators are based on weighted samples
that are neither independent nor identically distributed. Using a
new l...
Our article is concerned with adaptive sampling schemes for Bayesian inference that
update the proposal densities using previous iterates. We introduce a copula based
proposal density which is made ...
The Sensitivity of Respondent-driven Sampling Method
directed network hidden population network respondent-driven sampling RDS sensitivity
2010/3/10
Researchers in many scientific fields make inferences from individuals to larger groups. For many groups however,there is no list of members from which to take a random sample. Respondent-driven sampl...
Distilled Sensing:Adaptive Sampling for Sparse Detection and Estimation
Adaptive sampling model selection multiple testing sequentialdesign sparse recovery
2010/3/9
Adaptive sampling results in dramatic improvements in the re-
covery of sparse signals in white Gaussian noise. A sequential adap-
tive sampling-and-refinement procedure called distilled sensing (DS...
Distilled Sensing:Adaptive Sampling for Sparse Detection and Estimation
Distilled Sensing Adaptive Sampling Sparse Detection Estimation
2010/3/9
Adaptive sampling results in dramatic improvements in the re-
covery of sparse signals in white Gaussian noise. A sequential adap-
tive sampling-and-refinement procedure called distilled sensing (DS...
Asymptotically optimum estimation of a probability in inverse binomial sampling
Asymptotic properties Inverse binomial sampling Sequential es-timation.
2010/3/9
The optimum quality that can be asymptotically achieved in the estimation
of a probability p using inverse binomial sampling is considered in this paper.
A general definition of quality is used, in ...
Bayesian nonparametric analysis for a species sampling model with finitely many types
Bayesian nonparametric analysis species sampling model finitely many types
2010/3/9
We derive explicit Bayesian nonparametric analysis for a species sampling model with
finitely many types of Gibbs form of type = −1 recently introduced in Gnedin (2009).
Our results compleme...