Maria Anna Di Lucca,Alessandra Guglielmi,Peter Müller et al.
Maria Anna Di Lucca et al.
We introduce a model for a time series of continuous outcomes, that can be expressed as fully nonparametric regression or density regression on lagged terms. The model is based on a dependent Dirichlet process prior on a family of random pr...
Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits [0.03%]
用于拟合数量性状多重遗传模型的贝叶斯多项式回归模型
Harold Bae,Thomas Perls,Martin Steinberg et al.
Harold Bae et al.
We present a coherent Bayesian framework for selection of the most likely model from the five genetic models (genotypic, additive, dominant, co-dominant, and recessive) commonly used in genetic association studies. The approach uses a polyn...
Discussion of 'Bayesian Nonparametric Inference - Why and How', by Peter Müller and Riten Mitra [0.03%]
"贝叶斯非参数统计推断——为什么及如何做？（评论文章）"
Susan M Paddock,Terrance D Savitsky
Susan M Paddock
Spatial Bayesian Variable Selection Models on Functional Magnetic Resonance Imaging Time-Series Data [0.03%]
基于功能磁共振成像时间序列数据的空间贝叶斯变量选择模型
Kuo-Jung Lee,Galin L Jones,Brian S Caffo et al.
Kuo-Jung Lee et al.
A common objective of fMRI (functional magnetic resonance imaging) studies is to determine subject-specific areas of increased blood oxygenation level dependent (BOLD) signal contrast in response to a stimulus or task, and hence to infer re...
Commensurate Priors for Incorporating Historical Information in Clinical Trials Using General and Generalized Linear Models [0.03%]
基于临床试验中使用广义和广义线性模型的先验兼容性历史信息纳入方法研究
Brian P Hobbs,Daniel J Sargent,Bradley P Carlin
Brian P Hobbs
Assessing between-study variability in the context of conventional random-effects meta-analysis is notoriously difficult when incorporating data from only a small number of historical studies. In order to borrow strength, historical and cur...
On Numerical Aspects of Bayesian Model Selection in High and Ultrahigh-dimensional Settings [0.03%]
高维和超高维设定下贝叶斯模型选择的数值方面分析
Valen E Johnson
Valen E Johnson
This article examines the convergence properties of a Bayesian model selection procedure based on a non-local prior density in ultrahigh-dimensional settings. The performance of the model selection procedure is also compared to popular pena...
Peter Müller,Riten Mitra
Peter Müller
We review inference under models with nonparametric Bayesian (BNP) priors. The discussion follows a set of examples for some common inference problems. The examples are chosen to highlight problems that are challenging for standard parametr...
Multiple-Shrinkage Multinomial Probit Models with Applications to Simulating Geographies in Public Use Data [0.03%]
应用于公共使用的数据模拟地理区域的多重收缩多类别 probit 模型
Lane F Burgette,Jerome P Reiter
Lane F Burgette
Multinomial outcomes with many levels can be challenging to model. Information typically accrues slowly with increasing sample size, yet the parameter space expands rapidly with additional covariates. Shrinking all regression parameters tow...
Nonparametric Bayesian models through probit stick-breaking processes [0.03%]
通过probit stick-breaking过程的非参数贝叶斯模型
Abel Rodríguez,David B Dunson
Abel Rodríguez
We describe a novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables. We show that these priors are extremely ...
Prior Effective Sample Size in Conditionally Independent Hierarchical Models [0.03%]
条件独立层次模型中的先验有效样本量
Satoshi Morita,Peter F Thall,Peter Müller
Satoshi Morita
Prior effective sample size (ESS) of a Bayesian parametric model was defined by Morita, et al. (2008, Biometrics,64, 595-602). Starting with an ε-information prior defined to have the same means and correlations as the prior but to be vagu...