Bayesian Analysis最新文献

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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
{"title":"Inference for Bayesian Nonparametric Models with Binary Response Data via Permutation Counting","authors":"Dennis Christensen","doi":"10.1214/22-ba1353","DOIUrl":"https://doi.org/10.1214/22-ba1353","url":null,"abstract":"","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":"46134883","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
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
{"title":"Posterior Predictive Checking for Partially Observed Stochastic Epidemic Models","authors":"Georgios Aristotelous, T. Kypraios, P. O’Neill","doi":"10.1214/22-ba1336","DOIUrl":"https://doi.org/10.1214/22-ba1336","url":null,"abstract":"","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":"47907200","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
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
{"title":"Gaussian Variational Approximations for High-dimensional State Space Models","authors":"M. Quiroz, D. Nott, R. Kohn","doi":"10.1214/22-ba1332","DOIUrl":"https://doi.org/10.1214/22-ba1332","url":null,"abstract":"","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":"47451543","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: 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
{"title":"Normal Approximation for Bayesian Mixed Effects Binomial Regression Models","authors":"Brandon Berman, W. Johnson, Weining Shen","doi":"10.1214/22-ba1312","DOIUrl":"https://doi.org/10.1214/22-ba1312","url":null,"abstract":". Bayesian inference for generalized linear mixed models implemented with Markov chain Monte Carlo (MCMC) sampling methods have been widely used. In this paper, we propose to substitute a large sample normal approximation for the intractable full conditional distribution of the latent effects (of size k ) in order to simplify the computation. In addition, we develop a second approximation involving what we term a sufficient reduction (SR). We show that the full conditional distributions for the model parameters only depend on a small, say r (cid:2) k , dimensional function of the latent effects, and also that this reduction is asymptotically normal under mild conditions. Thus we substitute the sampling of an r dimensional multivariate normal for sampling the k dimensional full conditional for the latent effects. Applications to oncology physician data, to cow abortion data and simulation studies confirm the reasonable performance of the proposed approximation method in terms of estimation accuracy and computational speed.","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":"42285358","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
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
{"title":"A Multi-Armed Bayesian Ordinal Outcome Utility-Based Sequential Trial with a Pairwise Null Clustering Prior","authors":"A. Chapple, Yussef Bennani, Meredith Clement","doi":"10.1214/22-ba1316","DOIUrl":"https://doi.org/10.1214/22-ba1316","url":null,"abstract":". A multi-armed trial based on ordinal outcomes is proposed that lever-ages a flexible non-proportional odds cumulative logit model and numerical utility scores for each outcome to determine treatment optimality. This trial design uses a Bayesian clustering prior on the treatment effects that encourages the pairwise null hypothesis of no differences between treatments. A group sequential design is proposed to determine which treatments are clinically different with an adaptive decision boundary that becomes more aggressive as the sample size or clinical significance grows, or the number of active treatments decreases. A simulation study is conducted for 3 and 5 treatment arms, which shows that the design has superior operating characteristics (family wise error rate, generalized power, average sample size) compared to utility designs that do not allow clustering, a frequentist proportional odds model, or a permutation test based on empirical mean utilities.","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":"42427985","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
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
{"title":"Comparing Dependent Undirected Gaussian Networks","authors":"Hongmei Zhang, Xianzheng Huang, H. Arshad","doi":"10.1214/22-ba1337","DOIUrl":"https://doi.org/10.1214/22-ba1337","url":null,"abstract":"","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":"46042833","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
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
{"title":"Regularised B-splines Projected Gaussian Process Priors to Estimate Time-trends in Age-specific COVID-19 Deaths","authors":"M. Monod, A. Blenkinsop, A. Brizzi, Yu Chen, Carlos Cardoso Correia Perello, Vidoushee Jogarah, Yuanrong Wang, S. Flaxman, S. Bhatt, O. Ratmann","doi":"10.1214/22-ba1334","DOIUrl":"https://doi.org/10.1214/22-ba1334","url":null,"abstract":"","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":"49530440","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
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":"<p><p>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 <i>chained Markov melding</i>, 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.</p>","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
{"title":"Joint Random Partition Models for Multivariate Change Point Analysis","authors":"J. J. Quinlan, G. Page, Luis M. Castro","doi":"10.1214/22-ba1344","DOIUrl":"https://doi.org/10.1214/22-ba1344","url":null,"abstract":"","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":"42246140","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
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