Bayesian Analysis最新文献_第2页

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 3 2区数学

Bayesian Analysis Pub Date : 2024-06-01 Epub Date: 2024-04-09 DOI: 10.1214/22-ba1356

Shai Gorsky, Cliburn Chan, Li Ma

{"title":"Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses.","authors":"Shai Gorsky, Cliburn Chan, Li Ma","doi":"10.1214/22-ba1356","DOIUrl":"10.1214/22-ba1356","url":null,"abstract":"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 cytometry data involves cell classification-that is, the identification of cell subgroups in the sample-and comparisons of the cell subgroups across samples or conditions. While modern experiments often necessitate the collection and processing of samples in multiple batches, analysis of cytometry data across batches is challenging because differences across samples may occur due to either true biological variation or technical reasons such as antibody lot effects or instrument optics across batches. Thus a critical step in comparative analyses of multi-sample cytometry data-yet missing in existing automated methods for analyzing such data-is cross-sample calibration, whose goal is to align corresponding cell subsets across multiple samples in the presence of technical variations, so that biological variations can be meaningfully compared. We introduce a Bayesian nonparametric hierarchical modeling approach for accomplishing both calibration and cell classification simultaneously in a unified probabilistic manner. Three important features of our method make it particularly effective for analyzing multi-sample cytometry data: a nonparametric mixture avoids prespecifying the number of cell clusters; a hierarchical skew normal kernel that allows flexibility in the shapes of the cell subsets and cross-sample variation in their locations; and finally the \"coarsening\" strategy makes inference robust to departures from the model not captured by the skew normal kernels. We demonstrate the merits of our approach in simulated examples and carry out a case study in the analysis of a multi-sample cytometry data set. We provide an R package for our method.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":"439-463"},"PeriodicalIF":3.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13215703/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian Analysis of Exponential Random Graph Models Using Stochastic Gradient Markov Chain Monte Carlo. 基于随机梯度马尔可夫链蒙特卡罗的指数随机图模型的贝叶斯分析

IF 4.9 2区数学

Bayesian Analysis Pub Date : 2024-06-01 Epub Date: 2024-04-09 DOI: 10.1214/23-BA1364

Qian Zhang, Faming Liang

引用次数: 0

Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator. 利用泰晤士估计器从后验模拟中轻松计算边际似然。

IF 2.5 2区数学

Bayesian Analysis Pub Date : 2024-04-23 DOI: 10.1214/24-ba1422

Martin Metodiev, Marie Perrot-Dockès, Sarah Ouadah, Nicholas J Irons, Pierre Latouche, Adrian E Raftery

引用次数: 0

Fast Methods for Posterior Inference of Two-Group Normal-Normal Models. 两组正态模型后验推理的快速方法

IF 3 2区数学

Bayesian Analysis Pub Date : 2023-09-01 Epub Date: 2022-09-09 DOI: 10.1214/22-ba1329

Philip Greengard, Jeremy Hoskins, Charles C Margossian, Jonah Gabry, Andrew Gelman, Aki Vehtari

{"title":"Fast Methods for Posterior Inference of Two-Group Normal-Normal Models.","authors":"Philip Greengard, Jeremy Hoskins, Charles C Margossian, Jonah Gabry, Andrew Gelman, Aki Vehtari","doi":"10.1214/22-ba1329","DOIUrl":"10.1214/22-ba1329","url":null,"abstract":"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 mixed effects models with partial pooling over one group of predictors, as well as random effects models with partial pooling over two groups of predictors. We demonstrate the performance of the methods on two applications, one involving U.S. opinion polls and one involving the modeling of COVID-19 outbreaks in Israel using survey data. The algorithms involve analytical marginalization of regression coefficients followed by numerical integration of the remaining low-dimensional density. The dominant cost of the algorithms is an eigendecomposition computed once for each value of the outside parameter of integration. Our approach drastically reduces run times compared to state-of-the-art Markov chain Monte Carlo (MCMC) algorithms. The latter, in addition to being computationally expensive, can also be difficult to tune when applied to hierarchical models.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":"889-907"},"PeriodicalIF":3.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12442500/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46750531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reproducible Model Selection Using Bagged Posteriors. 利用袋装后验进行可重复性模型选择。

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2023-03-01 DOI: 10.1214/21-ba1301

Jonathan H Huggins, Jeffrey W Miller

{"title":"Reproducible Model Selection Using Bagged Posteriors.","authors":"Jonathan H Huggins, Jeffrey W Miller","doi":"10.1214/21-ba1301","DOIUrl":"https://doi.org/10.1214/21-ba1301","url":null,"abstract":"Bayesian model selection is premised on the assumption that the data are generated from one of the postulated models. However, in many applications, all of these models are incorrect (that is, there is misspecification). When the models are misspecified, two or more models can provide a nearly equally good fit to the data, in which case Bayesian model selection can be highly unstable, potentially leading to self-contradictory findings. To remedy this instability, we propose to use bagging on the posterior distribution (\"BayesBag\") - that is, to average the posterior model probabilities over many bootstrapped datasets. We provide theoretical results characterizing the asymptotic behavior of the posterior and the bagged posterior in the (misspecified) model selection setting. We empirically assess the BayesBag approach on synthetic and real-world data in (i) feature selection for linear regression and (ii) phylogenetic tree reconstruction. Our theory and experiments show that, when all models are misspecified, BayesBag (a) provides greater reproducibility and (b) places posterior mass on optimal models more reliably, compared to the usual Bayesian posterior; on the other hand, under correct specification, BayesBag is slightly more conservative than the usual posterior, in the sense that BayesBag posterior probabilities tend to be slightly farther from the extremes of zero and one. Overall, our results demonstrate that BayesBag provides an easy-to-use and widely applicable approach that improves upon Bayesian model selection by making it more stable and reproducible.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":"18 1","pages":"79-104"},"PeriodicalIF":4.4,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838736/pdf/nihms-1796997.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9229540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

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

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