Bayesian Analysis最新文献_第4页

Bayesian Learning of Graph Substructures 图子结构的贝叶斯学习

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-01-01 DOI: 10.1214/22-BA1338

W. V. Boom, M. Iorio, A. Beskos

{"title":"Bayesian Learning of Graph Substructures","authors":"W. V. Boom, M. Iorio, A. Beskos","doi":"10.1214/22-BA1338","DOIUrl":"https://doi.org/10.1214/22-BA1338","url":null,"abstract":"Graphical models provide a powerful methodology for learning the conditional independence structure in multivariate data. Inference is often focused on estimating individual edges in the latent graph. Nonetheless, there is increasing interest in inferring more complex structures, such as communities, for multiple reasons, including more effective information retrieval and better interpretability. Stochastic blockmodels offer a powerful tool to detect such structure in a network. We thus propose to exploit advances in random graph theory and embed them within the graphical models framework. A consequence of this approach is the propagation of the uncertainty in graph estimation to large-scale structure learning. We consider Bayesian nonparametric stochastic blockmodels as priors on the graph. We extend such models to consider clique-based blocks and to multiple graph settings introducing a novel prior process based on a Dependent Dirichlet process. Moreover, we devise a tailored computation strategy of Bayes factors for block structure based on the Savage-Dickey ratio to test for presence of larger structure in a graph. We demonstrate our approach in simulations as well as on real data applications in finance and transcriptomics.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44413832","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

Inference for Bayesian Nonparametric Models with Binary Response Data via Permutation Counting 二值响应数据下贝叶斯非参数模型的置换计数推理

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-01-01 DOI: 10.1214/22-ba1353

Dennis Christensen

引用次数: 1

Posterior Predictive Checking for Partially Observed Stochastic Epidemic Models 部分观测随机流行病模型的后验预测检验

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-01-01 DOI: 10.1214/22-ba1336

Georgios Aristotelous, T. Kypraios, P. O’Neill

引用次数: 0

Gaussian Variational Approximations for High-dimensional State Space Models 高维状态空间模型的高斯变分逼近

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-01-01 DOI: 10.1214/22-ba1332

M. Quiroz, D. Nott, R. Kohn

引用次数: 7

Normal Approximation for Bayesian Mixed Effects Binomial Regression Models 贝叶斯混合效应二项回归模型的正态逼近

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-01-01 DOI: 10.1214/22-ba1312

Brandon Berman, W. Johnson, Weining Shen

引用次数: 2

A Multi-Armed Bayesian Ordinal Outcome Utility-Based Sequential Trial with a Pairwise Null Clustering Prior 具有成对零聚类先验的多臂贝叶斯有序结果效用序贯试验

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-01-01 DOI: 10.1214/22-ba1316

A. Chapple, Yussef Bennani, Meredith Clement

引用次数: 0

Comparing Dependent Undirected Gaussian Networks 比较相关无向高斯网络

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-01-01 DOI: 10.1214/22-ba1337

Hongmei Zhang, Xianzheng Huang, H. Arshad

引用次数: 2

Regularised B-splines Projected Gaussian Process Priors to Estimate Time-trends in Age-specific COVID-19 Deaths 正则B样条投影高斯过程先验估计特定年龄新冠肺炎死亡的时间趋势

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-01-01 DOI: 10.1214/22-ba1334

M. Monod, A. Blenkinsop, A. Brizzi, Yu Chen, Carlos Cardoso Correia Perello, Vidoushee Jogarah, Yuanrong Wang, S. Flaxman, S. Bhatt, O. Ratmann

引用次数: 1

Combining chains of Bayesian models with Markov melding. 用马尔科夫混合法组合贝叶斯模型链。

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-01-01 DOI: 10.1214/22-BA1327

Andrew A Manderson, Robert J B Goudie

{"title":"Combining chains of Bayesian models with Markov melding.","authors":"Andrew A Manderson, Robert J B Goudie","doi":"10.1214/22-BA1327","DOIUrl":"10.1214/22-BA1327","url":null,"abstract":"A challenge for practitioners of Bayesian inference is specifying a model that incorporates multiple relevant, heterogeneous data sets. It may be easier to instead specify distinct submodels for each source of data, then join the submodels together. We consider chains of submodels, where submodels directly relate to their neighbours via common quantities which may be parameters or deterministic functions thereof. We propose chained Markov melding, an extension of Markov melding, a generic method to combine chains of submodels into a joint model. One challenge we address is appropriately capturing the prior dependence between common quantities within a submodel, whilst also reconciling differences in priors for the same common quantity between two adjacent submodels. Estimating the posterior of the resulting overall joint model is also challenging, so we describe a sampler that uses the chain structure to incorporate information contained in the submodels in multiple stages, possibly in parallel. We demonstrate our methodology using two examples. The first example considers an ecological integrated population model, where multiple data sets are required to accurately estimate population immigration and reproduction rates. We also consider a joint longitudinal and time-to-event model with uncertain, submodel-derived event times. Chained Markov melding is a conceptually appealing approach to integrating submodels in these settings.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":"18 3","pages":"807-840"},"PeriodicalIF":4.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10010662","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

Joint Random Partition Models for Multivariate Change Point Analysis 多元变点分析的联合随机划分模型

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-01-01 DOI: 10.1214/22-ba1344

J. J. Quinlan, G. Page, Luis M. Castro

引用次数: 0