Bayesian Dose Finding for Combined Drugs with Discrete and Continuous Doses [0.03%]
联合用药的离散和连续剂量的贝叶斯剂量探索方法研究
Lin Huo,Ying Yuan,Guosheng Yin
Lin Huo
The trend of treating patients with combined drugs has grown in cancer clinical trials. Often, evaluating the synergism of multiple drugs is the primary motivation for such drug-combination studies. To enhance the patient response, a new ca...
Spiked Dirichlet Process Prior for Bayesian Multiple Hypothesis Testing in Random Effects Models [0.03%]
随机效应模型中贝叶斯多重假设检验的加权狄利克雷过程先验
Sinae Kim,David B Dahl,Marina Vannucci
Sinae Kim
We propose a Bayesian method for multiple hypothesis testing in random effects models that uses Dirichlet process (DP) priors for a nonparametric treatment of the random effects distribution. We consider a general model formulation which ac...
Nonparametric Bayesian Segmentation of a Multivariate Inhomogeneous Space-Time Poisson Process [0.03%]
非参数贝叶斯多变量空间时间泊松过程的分段算法
Mingtao Ding,Lihan He,David Dunson et al.
Mingtao Ding et al.
A nonparametric Bayesian model is proposed for segmenting time-evolving multivariate spatial point process data. An inhomogeneous Poisson process is assumed, with a logistic stick-breaking process (LSBP) used to encourage piecewise-constant...
Simultaneous Bayesian inference for skew-normal semiparametric nonlinear mixed-effects models with covariate measurement errors [0.03%]
含测量误差协变量的偏斜正态半参数非线性混合效应模型的Bayes推断
Yangxin Huang,Getachew A Dagne
Yangxin Huang
Longitudinal data arise frequently in medical studies and it is a common practice to analyze such complex data with nonlinear mixed-effects (NLME) models which enable us to account for between-subject and within-subject variations. To parti...
A Bayesian Semiparametric Temporally-Stratified Proportional Hazards Model with Spatial Frailties [0.03%]
具有空间风向的风险的贝叶斯半参时间分层比例危险模型
Timothy E Hanson,Alejandro Jara,Luping Zhao
Timothy E Hanson
Incorporating temporal and spatial variation could potentially enhance information gathered from survival data. This paper proposes a Bayesian semiparametric model for capturing spatio-temporal heterogeneity within the proportional hazards ...
Fernando A Quintana,Peter Müler,Gary L Rosner et al.
Fernando A Quintana et al.
We analyze complete sequences of successes (hits, walks, and sacrifices) for a group of players from the American and National Leagues, collected over 4 seasons. The goal is to describe how players' performances vary from season to season. ...
Hierarchical Bayesian nonparametric mixture models for clustering with variable relevance determination [0.03%]
分层贝叶斯非参数混合模型及变量相关性确定在聚类中的应用
Christopher Yau,Chris Holmes
Christopher Yau
We propose a hierarchical Bayesian nonparametric mixture model for clustering when some of the covariates are assumed to be of varying relevance to the clustering problem. This can be thought of as an issue in variable selection for unsuper...
Bayesian Hierarchical Multiresolution Hazard Model for the Study of Time-Dependent Failure Patterns in Early Stage Breast Cancer [0.03%]
早期乳腺癌时间依赖性失效模式的贝叶斯分层多分辨率危险度模型研究
Vanja Dukić,James Dignam
Vanja Dukić
The multiresolution estimator, developed originally in engineering applications as a wavelet-based method for density estimation, has been recently extended and adapted for estimation of hazard functions (Bouman et al. 2005, 2007). Using th...
Spatial Mixture Modelling for Unobserved Point Processes: Examples in Immunofluorescence Histology [0.03%]
空间混合模型在未观察到的点过程中的应用：免疫荧光组织学示例
Chunlin Ji,Daniel Merl,Thomas B Kepler et al.
Chunlin Ji et al.
We discuss Bayesian modelling and computational methods in analysis of indirectly observed spatial point processes. The context involves noisy measurements on an underlying point process that provide indirect and noisy data on locations of ...
Selection Sampling from Large Data Sets for Targeted Inference in Mixture Modeling [0.03%]
从大型数据集中选择性抽样以在混合建模中进行目标推断
Ioanna Manolopoulou,Cliburn Chan,Mike West
Ioanna Manolopoulou
One of the challenges in using Markov chain Monte Carlo for model analysis in studies with very large datasets is the need to scan through the whole data at each iteration of the sampler, which can be computationally prohibitive. Several ap...