Yuling Yao,Gregor Pirš,Aki Vehtari et al.
Yuling Yao et al.
Stacking is a widely used model averaging technique that asymptotically yields optimal predictions among linear averages. We show that stacking is most effective when model predictive performance is heterogeneous in inputs, and we can furth...
Nicholas J Irons,Carlos Cinelli
Nicholas J Irons
We introduce the BREASE framework for the Bayesian analysis of randomized controlled trials with binary treatment and outcome. Approaching the problem from a causal inference perspective, we propose parameterizing the likelihood in terms of...
Bag of DAGs: Inferring Directional Dependence in Spatiotemporal Processes [0.03%]
DAG的集合：推断时空过程中的单向依赖关系
Bora Jin,Michele Peruzzi,David Dunson
Bora Jin
We propose a class of nonstationary processes to characterize space- and time-varying directional associations in point-referenced data. We are motivated by spatiotemporal modeling of air pollutants in which local wind patterns are key dete...
Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator [0.03%]
基于后验模拟计算边际似然的THAMES估计器
Martin Metodiev,Marie Perrot-Dockès,Sarah Ouadah et al.
Martin Metodiev et al.
We propose an easily computed estimator of marginal likelihoods from posterior simulation output, via reciprocal importance sampling, combining earlier proposals of DiCiccio et al (1997) and Robert and Wraith (2009). This involves only the ...
Nonparametric Bayes Differential Analysis of Multigroup DNA Methylation Data [0.03%]
多组DNA甲基化数据的非参数湾ес差分分析
Chiyu Gu,Veerabhadran Baladandayuthapani,Subharup Guha
Chiyu Gu
DNA methylation datasets in cancer studies are comprised of measurements on a large number of genomic locations called cytosine-phosphate-guanine (CpG) sites with complex correlation structures. A fundamental goal of these studies is the de...
Bayesian Analysis of Exponential Random Graph Models Using Stochastic Gradient Markov Chain Monte Carlo [0.03%]
基于随机梯度马尔科夫链蒙特卡洛的指数随机图模型的贝叶斯分析方法研究
Qian Zhang,Faming Liang
Qian Zhang
The exponential random graph model (ERGM) is a popular model for social networks, which is known to have an intractable likelihood function. Sampling from the posterior for such a model is a long-standing problem in statistical research. We...
Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors [0.03%]
基于 spiked ewens-pitman 吸引先验的函数并行回归混合模型
Mingrui Liang,Matthew D Koslovsky,Emily T Hébert et al.
Mingrui Liang et al.
Functional concurrent, or varying-coefficient, regression models are a form of functional data analysis methods in which functional covariates and outcomes are collected concurrently. Two active areas of research for this class of models ar...
A General Bayesian Functional Spatial Partitioning Method for Multiple Region Discovery Applied to Prostate Cancer MRI [0.03%]
应用于前列腺癌MRI的多重区域发现的通用贝叶斯函数空间划分方法
Maria Masotti,Lin Zhang,Gregory J Metzger et al.
Maria Masotti et al.
Current protocols to estimate the number, size, and location of cancerous lesions in the prostate using multiparametric magnetic resonance imaging (mpMRI) are highly dependent on reader experience and expertise. Automatic voxel-wise cancer ...
Shrinkage with shrunken shoulders: Gibbs sampling shrinkage model posteriors with guaranteed convergence rates [0.03%]
具有效率保证的收缩模型后验分布的Gibbs抽样方法
Akihiko Nishimura,Marc A Suchard
Akihiko Nishimura
Use of continuous shrinkage priors - with a "spike" near zero and heavy-tails towards infinity - is an increasingly popular approach to induce sparsity in parameter estimates. When the parameters are only weakly identified by the likelihood...
How Trustworthy Is Your Tree? Bayesian Phylogenetic Effective Sample Size Through the Lens of Monte Carlo Error [0.03%]
您的树图有多可靠？通过蒙特卡洛误差分析贝叶斯系统发育有效样本量
Andrew Magee,Michael Karcher,Frederick A Matsen th et al.
Andrew Magee et al.
Bayesian inference is a popular and widely-used approach to infer phylogenies (evolutionary trees). However, despite decades of widespread application, it remains difficult to judge how well a given Bayesian Markov chain Monte Carlo (MCMC) ...