A Tree Perspective on Stick-Breaking Models in Covariate-Dependent Mixtures (with Discussion) [0.03%]
基于 covariate-依赖混合的拆杆模型的树方法（并附讨论）
Akira Horiguchi,Cliburn Chan,Li Ma
Akira Horiguchi
Stick-breaking (SB) processes are often adopted in Bayesian mixture models for generating mixing weights. When covariates influence the sizes of clusters, SB mixtures are particularly convenient as they can leverage their connection to bina...
Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses [0.03%]
基于粗化分层斜Normal核的混合物模型及其在流动/海量式细胞分析中的应用
Shai Gorsky,Cliburn Chan,Li Ma
Shai Gorsky
Cytometry is the standard multi-parameter assay for measuring single cell phenotype and functionality. It is commonly used for quantifying the relative frequencies of cell subsets in blood and disaggregated tissues. A typical analysis of cy...
Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking [0.03%]
基于预测叠加的时空非高斯数据的贝叶斯推断方法研究
Soumyakanti Pan,Lu Zhang,Jonathan R Bradley et al.
Soumyakanti Pan et al.
Analysing non-Gaussian spatial-temporal data requires introducing spatial as well as temporal dependence in generalised linear models through the link function of an exponential family distribution. Unlike in Gaussian likelihoods, inference...
Hongmei Zhang,Xianzheng Huang,Jianjun Gan et al.
Hongmei Zhang et al.
We present a Bayesian variable selection method based on an extension of the Zellner's g -prior in linear models. More specifically, we propose a two-component G -prior, wherein a tuning parameter, calibrated by use of pseudo-variables, is ...
Logistic-Beta Processes for Dependent Random Probabilities with Beta Marginals [0.03%]
用于具有Beta边缘的依赖随机概率的逻辑Beta过程
Changwoo J Lee,Alessandro Zito,Huiyan Sang et al.
Changwoo J Lee et al.
The beta distribution serves as a canonical tool for modeling probabilities in statistics and machine learning. However, there is limited work on flexible and computationally convenient stochastic process extensions for modeling dependent r...
Gridding and Parameter Expansion for Scalable Latent Gaussian Models of Spatial Multivariate Data [0.03%]
用于空间多元数据的可扩展潜在高斯模型的栅格化和参数扩充方法研究
Michele Peruzzi,Sudipto Banerjee,David B Dunson et al.
Michele Peruzzi et al.
Scalable spatial GPs for massive datasets can be built via sparse Directed Acyclic Graphs (DAGs) where a small number of directed edges is sufficient to flexibly characterize spatial dependence. The DAG can be used to devise fast algorithms...
Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity [0.03%]
基于模拟的贝叶斯计算校准检验：检验量的选择影响敏感度分析
Martin Modrák,Angie H Moon,Shinyoung Kim et al.
Martin Modrák et al.
Simulation-based calibration checking (SBC) is a practical method to validate computationally-derived posterior distributions or their approximations. In this paper, we introduce a new variant of SBC to alleviate several known problems. Our...
Chin-Yi Lin,Kuo-Chin Chen,Philippe Lemey et al.
Chin-Yi Lin et al.
Multiproposal Markov chain Monte Carlo (MCMC) algorithms choose from multiple proposals to generate their next chain step in order to sample from challenging target distributions more efficiently. However, on classical machines, these algor...
Exploiting Multivariate Network Meta-Analysis: A Calibrated Bayesian Composite Likelihood Inference [0.03%]
多元网络 meta 分析的贝叶斯组合似然推断及其校准
Yifei Wang,Lifeng Lin,Yu-Lun Liu
Yifei Wang
Multivariate network meta-analysis has emerged as a powerful tool for evidence synthesis by incorporating multiple outcomes and treatments. Despite its advantages, this method comes with methodological challenges, such as the issue of unrep...
Philip Greengard,Jeremy Hoskins,Charles C Margossian et al.
Philip Greengard et al.
We describe a class of algorithms for evaluating posterior moments of certain Bayesian linear regression models with a normal likelihood and a normal prior on the regression coefficients. The proposed methods can be used for hierarchical mi...