Bayesian Analysis最新文献_第2页

A Bayesian Nonparametric Latent Space Approach to Modeling Evolving Communities in Dynamic Networks 动态网络中演化群体建模的贝叶斯非参数隐空间方法

2区数学

Bayesian Analysis Pub Date : 2023-03-01 DOI: 10.1214/21-ba1300

Joshua Daniel Loyal, Yuguo Chen

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引用次数: 3

A Latent Shrinkage Position Model for Binary and Count Network Data 二进制和计数网络数据的潜在收缩位置模型

2区数学

Bayesian Analysis Pub Date : 2023-01-01 DOI: 10.1214/23-ba1403

Xian Yao Gwee, Isobel Claire Gormley, Michael Fop

{"title":"A Latent Shrinkage Position Model for Binary and Count Network Data","authors":"Xian Yao Gwee, Isobel Claire Gormley, Michael Fop","doi":"10.1214/23-ba1403","DOIUrl":"https://doi.org/10.1214/23-ba1403","url":null,"abstract":"Interactions between actors are frequently represented using a network. The latent position model is widely used for analysing network data, whereby each actor is positioned in a latent space. Inferring the dimension of this space is challenging. Often, for simplicity, two dimensions are used or model selection criteria are employed to select the dimension, but this requires choosing a criterion and the computational expense of fitting multiple models. Here the latent shrinkage position model (LSPM) is proposed which intrinsically infers the effective dimension of the latent space. The LSPM employs a Bayesian nonparametric multiplicative truncated gamma process prior that ensures shrinkage of the variance of the latent positions across higher dimensions. Dimensions with non-negligible variance are deemed most useful to describe the observed network, inducing automatic inference on the latent space dimension. While the LSPM is applicable to many network types, logistic and Poisson LSPMs are developed here for binary and count networks respectively. Inference proceeds via a Markov chain Monte Carlo algorithm, where novel surrogate proposal distributions reduce the computational burden. The LSPM’s properties are assessed through simulation studies, and its utility is illustrated through application to real network datasets. Open source software assists wider implementation of the LSPM.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135051558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Fast Bayesian Functional Regression for Non-Gaussian Spatial Data 非高斯空间数据的快速贝叶斯函数回归

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2023-01-01 DOI: 10.1214/22-ba1354

Hyun Bin Kang, Yeo Jin Jung, Jaewoo Park

引用次数: 2

On the Use of a Local Rˆ to Improve MCMC Convergence Diagnostic 利用局部R -改进MCMC收敛诊断

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2023-01-01 DOI: 10.1214/23-ba1399

Théo Moins, Julyan Arbel, A. Dutfoy, S. Girard

引用次数: 8

Objective Bayesian Model Selection for Spatial Hierarchical Models with Intrinsic Conditional Autoregressive Priors 目的基于条件自回归先验的空间层次模型的贝叶斯模型选择

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2023-01-01 DOI: 10.1214/23-ba1375

Erica M. Porter, C. Franck, Marco A. R. Ferreira

引用次数: 1

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses 用于流式细胞术和流式细胞术分析的分层倾斜正态核的粗化混合物

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2023-01-01 DOI: 10.1214/22-ba1356

S. Gorsky, Cliburn Chan, Li Ma

引用次数: 1

Warped Gradient-Enhanced Gaussian Process Surrogate Models for Exponential Family Likelihoods with Intractable Normalizing Constants 具有难以处理的归一化常数的指数族似然的扭曲梯度增强高斯过程代理模型

2区数学

Bayesian Analysis Pub Date : 2023-01-01 DOI: 10.1214/23-ba1400

Quan Vu, Matthew T. Moores, Andrew Zammit-Mangion

{"title":"Warped Gradient-Enhanced Gaussian Process Surrogate Models for Exponential Family Likelihoods with Intractable Normalizing Constants","authors":"Quan Vu, Matthew T. Moores, Andrew Zammit-Mangion","doi":"10.1214/23-ba1400","DOIUrl":"https://doi.org/10.1214/23-ba1400","url":null,"abstract":"Markov chain Monte Carlo methods for exponential family models with intractable normalizing constant, such as the exchange algorithm, require simulations of the sufficient statistics at every iteration of the Markov chain, which often result in expensive computations. Surrogate models for the likelihood function have been developed to accelerate inference algorithms in this context. However, these surrogate models tend to be relatively inflexible, and often provide a poor approximation to the true likelihood function. In this article, we propose the use of a warped, gradient-enhanced, Gaussian process surrogate model for the likelihood function, which jointly models the sample means and variances of the sufficient statistics, and uses warping functions to capture covariance nonstationarity in the input parameter space. We show that both the consideration of nonstationarity and the inclusion of gradient information can be leveraged to obtain a surrogate model that outperforms the conventional stationary Gaussian process surrogate model when making inference, particularly in regions where the likelihood function exhibits a phase transition. We also show that the proposed surrogate model can be used to improve the effective sample size per unit time when embedded in exact inferential algorithms. The utility of our approach in speeding up inferential algorithms is demonstrated on simulated and real-world data.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135103369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Objective Bayesian Meta-Analysis Based on Generalized Marginal Multivariate Random Effects Model 基于广义边际多变量随机效应模型的目标贝叶斯元分析

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2023-01-01 DOI: 10.1214/23-ba1363

Olha Bodnar, Taras Bodnar

引用次数: 2

Bayesian Optimal Two-Sample Tests for High-Dimensional Gaussian Populations 高维高斯总体的贝叶斯最优两样本检验

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2023-01-01 DOI: 10.1214/23-ba1373

Kyoungjae Lee, Kisung You, Lizhen Lin

引用次数: 1

A Bayesian Approach for Spatio-Temporal Data-Driven Dynamic Equation Discovery 时空数据驱动动态方程发现的贝叶斯方法