搜索结果: 1-15 共查到“数理统计学 Bayesian”相关记录23条 . 查询时间(0.142 秒)
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Bayesian sequential design for sensitivity experiments with hybrid responses
混合响应 灵敏度实验 贝叶斯顺序设计
2023/4/25
In experimental design, a common problem seen in practice is when the result includes one binary response and multiple continuous responses. However, this problem receives scant attention. Most studie...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Fixed Effects Bayesian Testing in High-Dimensional Linear Mixed Models
高维 线性混合模型 固定效应 贝叶斯检验
2023/5/5
Hypothesis testing on high-dimensional fixed effects is indispensable for investigating the utility of the predictors on response. In this case, the conventional frequentist methods designed for cases...
2018高维统计模型的贝叶斯计算研讨会（Workshop on Bayesian Computation for High-Dimensional Statistical Models）
2018 高维统计模型的贝叶斯计算 研讨会
2017/12/20
In recent years there has been an explosion of complex data-sets in areas as diverse as Bioinformatics, Ecology, Epidemiology, Finance, subsurface Geophysics, Meteorology, and Population genetics. In ...
MODERN SCIENCE AND THE BAYESIAN-FREQUENTIST CONTROVERSY
BAYESIAN-FREQUENTIST CONTROVERSY MODERN SCIENCE
2015/8/20
The 250-year debate between Bayesians and frequentists is unusual among philsophical arguments in actually having important practical consequences. Whenever
noisy data is a major concern, scientists ...
In the absence of relevant prior experience, popular Bayesian estimation techniques usually
begin with some form of \uninformative" prior distribution intended to have minimal inferential
in
uence....
Bayesian Inference and the Parametric Bootstrap。
Terrestrial support of zebra mussels and the Hudson River food web: A multi-isotope, Bayesian analysis
Terrestrial support of zebra mussels Hudson River food web A multi-isotope Bayesian analysis
2014/4/2
The Hudson River is a strongly heterotrophic system in which the invasive zebra mussel (Dreissena polymorpha) comprises >90% of total metazoan biomass. Using a Bayesian mixing model, with isotope rati...
Scoring Bayesian Networks with Informative, Causal and Associative Priors
Scoring Bayesian Networks Informative Causal Associative Priors
2012/11/26
A significant theoretical advantage of search-and-score methods for learning Bayesian Networks is that they can accept informative prior beliefs for each possible network, thus complementing the data....
A Bayesian Nonparametric Approach to Image Super-resolution
Bayesian nonparametrics factor analysis dictionary learning variational inference gibbs sampling stochastic optimization,image super-resolution.
2012/11/26
Super-resolution methods form high-resolution images from low-resolution images. In this paper, we develop a new Bayesian nonparametric model for super-resolution. Our method uses a beta-Bernoulli pro...
Polygenic Modeling with Bayesian Sparse Linear Mixed Models
Polygenic Modeling Bayesian Sparse Linear Mixed Models
2012/11/23
Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies. These two approaches ...
In this paper we review recent advances in Stable Isotope Mixing Models (SIMMs) and place them into an over-arching Bayesian statistical framework which allows for several useful extensions. SIMMs are...
Nonparametric Bayesian methods for one-dimensional diffusion models: an overview of recent developments
Nonparametric Bayesian methods one-dimensional diffusion models overview of recent developments
2012/11/23
In this paper we review recently developed methods for nonparametric Bayesian inference for one-dimensional diffusion models. We discuss different possible prior distributions, computational issues, a...
Bayesian inverse problems with non-conjugate priors
Rate of contraction posterior distribution nonparametric hypothesis testing.
2012/11/23
We investigate the frequentist posterior contraction rate of nonparametric Bayesian procedures in linear inverse problems in both the mildly and severely ill-posed cases. A theorem is proved in a gene...
Learning Linear Bayesian Networks with Latent Variables
Linear Networks Bayesian Latent Variables
2012/11/23
This work considers the problem of learning linear Bayesian networks when some of the variables are unobserved. Identifiability and efficient recovery from low-order observable moments are established...
Bayesian Analysis of Simple Random Densities
Bayesian nonparametrics Bayesian density estimation.
2012/11/23
A tractable nonparametric prior over densities is introduced which is closed under sampling and exhibits proper posterior asymptotics.