Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior [0.03%]
通过阈值图拉普拉斯高斯先验的贝叶斯网络标记选择
Qingpo Cai,Jian Kang,Tianwei Yu
Qingpo Cai
Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this...
Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling [0.03%]
分层规范完全随机测度的稳健图形模型
Andrea Cremaschi,Raffaele Argiento,Katherine Shoemaker et al.
Andrea Cremaschi et al.
Gaussian graphical models are useful tools for exploring network structures in multivariate normal data. In this paper we are interested in situations where data show departures from Gaussianity, therefore requiring alternative modeling dis...
High-Dimensional Confounding Adjustment Using Continuous Spike and Slab Priors [0.03%]
基于连续尖 slab 先验的高维混杂调整
Joseph Antonelli,Giovanni Parmigiani,Francesca Dominici
Joseph Antonelli
In observational studies, estimation of a causal effect of a treatment on an outcome relies on proper adjustment for confounding. If the number of the potential confounders (p) is larger than the number of observations (n), then direct cont...
Fast Model-Fitting of Bayesian Variable Selection Regression Using the Iterative Complex Factorization Algorithm [0.03%]
迭代复因数分解算法在贝叶斯变量选择回归模型拟合中的快速应用
Quan Zhou,Yongtao Guan
Quan Zhou
Bayesian variable selection regression (BVSR) is able to jointly analyze genome-wide genetic datasets, but the slow computation via Markov chain Monte Carlo (MCMC) hampered its wide-spread usage. Here we present a novel iterative method to ...
Catherine A Calder,Candace Berrett
Catherine A Calder
We provide a discussion of the article "Computationally Efficient Multivariate Spatio-Temporal Models for High-Dimensional Count-Valued Data" by Bradley, Holan, and Wikle. In our opinion, this work constitutes a major contribution to the fi...
Robert J B Goudie,Anne M Presanis,David Lunn et al.
Robert J B Goudie et al.
Analysing multiple evidence sources is often feasible only via a modular approach, with separate submodels specified for smaller components of the available evidence. Here we introduce a generic framework that enables fully Bayesian analysi...
Yu-Bo Wang,Ming-Hui Chen,Lynn Kuo et al.
Yu-Bo Wang et al.
Evaluating the marginal likelihood in Bayesian analysis is essential for model selection. Estimators based on a single Markov chain Monte Carlo sample from the posterior distribution include the harmonic mean estimator and the inflated dens...
Locally Adaptive Smoothing with Markov Random Fields and Shrinkage Priors [0.03%]
基于马尔可夫随机场和收缩先验的局部自适应平滑方法
James R Faulkner,Vladimir N Minin
James R Faulkner
We present a locally adaptive nonparametric curve fitting method that operates within a fully Bayesian framework. This method uses shrinkage priors to induce sparsity in order-k differences in the latent trend function, providing a combinat...
Miguel A Martinez-Beneito,Paloma Botella-Rocamora,Sudipto Banerjee
Miguel A Martinez-Beneito
Multivariate disease mapping enriches traditional disease mapping studies by analysing several diseases jointly. This yields improved estimates of the geographical distribution of risk from the diseases by enabling borrowing of information ...
Sudipto Banerjee
Sudipto Banerjee
With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and ti...