Bayesian Analysis最新文献

筛选
英文 中文
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
{"title":"A Bayesian Nonparametric Latent Space Approach to Modeling Evolving Communities in Dynamic Networks","authors":"Joshua Daniel Loyal, Yuguo Chen","doi":"10.1214/21-ba1300","DOIUrl":"https://doi.org/10.1214/21-ba1300","url":null,"abstract":"The evolution of communities in dynamic (time-varying) network data is a prominent topic of interest. A popular approach to understanding these dynamic networks is to embed the dyadic relations into a latent metric space. While methods for clustering with this approach exist for dynamic networks, they all assume a static community structure. This paper presents a Bayesian nonparametric model for dynamic networks that can model networks with evolving community structures. Our model extends existing latent space approaches by explicitly modeling the additions, deletions, splits, and mergers of groups with a hierarchical Dirichlet process hidden Markov model. Our proposed approach, the hierarchical Dirichlet process latent position cluster model (HDP-LPCM), incorporates transitivity, models both individual and group level aspects of the data, and avoids the computationally expensive selection of the number of groups required by most popular methods. We provide a Markov chain Monte Carlo estimation algorithm and demonstrate its ability to detect evolving community structure in a network of military alliances during the Cold War and a narrative network constructed from the Game of Thrones television series.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":"285 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135643392","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: 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
{"title":"Fast Bayesian Functional Regression for Non-Gaussian Spatial Data","authors":"Hyun Bin Kang, Yeo Jin Jung, Jaewoo Park","doi":"10.1214/22-ba1354","DOIUrl":"https://doi.org/10.1214/22-ba1354","url":null,"abstract":"","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48879362","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: 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
{"title":"On the Use of a Local Rˆ to Improve MCMC Convergence Diagnostic","authors":"Théo Moins, Julyan Arbel, A. Dutfoy, S. Girard","doi":"10.1214/23-ba1399","DOIUrl":"https://doi.org/10.1214/23-ba1399","url":null,"abstract":"Diagnosing convergence of Markov chain Monte Carlo is crucial and remains an essentially unsolved problem. Among the most popular methods, the potential scale reduction factor, commonly named ˆ R , is an indicator that monitors the convergence of output chains to a target distribution, based on a comparison of the between- and within-variances. Several improvements have been suggested since its introduction in the 90s. Here, we aim at better understanding the ˆ R behavior by proposing a localized version that focuses on quantiles of the target distribution. This new version relies on key theoretical properties of the associated population value. It naturally leads to proposing a new indicator ˆ R ∞ , which is shown to allow both for localizing the Markov chain Monte Carlo convergence in different quantiles of the target distribution, and at the same time for handling some convergence issues not detected by other ˆ R versions.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44085501","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: 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
{"title":"Objective Bayesian Model Selection for Spatial Hierarchical Models with Intrinsic Conditional Autoregressive Priors","authors":"Erica M. Porter, C. Franck, Marco A. R. Ferreira","doi":"10.1214/23-ba1375","DOIUrl":"https://doi.org/10.1214/23-ba1375","url":null,"abstract":"","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42465458","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
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
{"title":"Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses","authors":"S. Gorsky, Cliburn Chan, Li Ma","doi":"10.1214/22-ba1356","DOIUrl":"https://doi.org/10.1214/22-ba1356","url":null,"abstract":"","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47319315","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
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
{"title":"Objective Bayesian Meta-Analysis Based on Generalized Marginal Multivariate Random Effects Model","authors":"Olha Bodnar, Taras Bodnar","doi":"10.1214/23-ba1363","DOIUrl":"https://doi.org/10.1214/23-ba1363","url":null,"abstract":"","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45398510","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: 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
{"title":"Bayesian Optimal Two-Sample Tests for High-Dimensional Gaussian Populations","authors":"Kyoungjae Lee, Kisung You, Lizhen Lin","doi":"10.1214/23-ba1373","DOIUrl":"https://doi.org/10.1214/23-ba1373","url":null,"abstract":"","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49607172","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
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
2区 数学
Bayesian Analysis Pub Date : 2023-01-01 DOI: 10.1214/23-ba1406
Joshua S. North, Christopher K. Wikle, Erin M. Schliep
{"title":"A Bayesian Approach for Spatio-Temporal Data-Driven Dynamic Equation Discovery","authors":"Joshua S. North, Christopher K. Wikle, Erin M. Schliep","doi":"10.1214/23-ba1406","DOIUrl":"https://doi.org/10.1214/23-ba1406","url":null,"abstract":"Differential equations based on physical principals are used to represent complex dynamic systems in all fields of science and engineering. When known, these equations have been shown to well represent real-world dynamics. However, since the true dynamics of complex systems are generally unknown, learning the governing equations can improve our understanding of the mechanisms driving the systems. Here, we develop a Bayesian approach to data-driven discovery of nonlinear spatio-temporal dynamic equations. Our approach can accommodate measurement error and missing data, both of which are common in real-world data, and accounts for parameter uncertainty. The proposed framework is illustrated using three simulated systems with varying amounts of measurement uncertainty and missing data and applied to a real-world system to infer the temporal evolution of the vorticity of the streamfunction.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":"6 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":"135051937","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: 2
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信