Bayesian Analysis最新文献_第5页

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

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":null,"pages":null},"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

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

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

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

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

Quantification of Empirical Determinacy: The Impact of Likelihood Weighting on Posterior Location and Spread in Bayesian Meta-Analysis Estimated with JAGS and INLA 经验确定性的量化：JAGS和INLA估计的贝叶斯元分析中似然加权对后验位置和传播的影响

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-01-01 DOI: 10.1214/22-ba1325

Hunanyan Sona, Rue Håvard, Plummer Martyn, Roos Małgorzata

{"title":"Quantification of Empirical Determinacy: The Impact of Likelihood Weighting on Posterior Location and Spread in Bayesian Meta-Analysis Estimated with JAGS and INLA","authors":"Hunanyan Sona, Rue Håvard, Plummer Martyn, Roos Małgorzata","doi":"10.1214/22-ba1325","DOIUrl":"https://doi.org/10.1214/22-ba1325","url":null,"abstract":"The popular Bayesian meta-analysis expressed by Bayesian normal-normal hierarchical model (NNHM) synthesizes knowledge from several studies and is highly relevant in practice. Moreover, NNHM is the simplest Bayesian hierarchical model (BHM), which illustrates problems typical in more complex BHMs. Until now, it has been unclear to what extent the data determines the marginal posterior distributions of the parameters in NNHM. To address this issue we computed the second derivative of the Bhattacharyya coefficient with respect to the weighted likelihood, defined the total empirical determinacy (TED), the proportion of the empirical determinacy of location to TED (pEDL), and the proportion of the empirical determinacy of spread to TED (pEDS). We implemented this method in the R package texttt{ed4bhm} and considered two case studies and one simulation study. We quantified TED, pEDL and pEDS under different modeling conditions such as model parametrization, the primary outcome, and the prior. This clarified to what extent the location and spread of the marginal posterior distributions of the parameters are determined by the data. Although these investigations focused on Bayesian NNHM, the method proposed is applicable more generally to complex BHMs.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49057422","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

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

Bayesian Estimation of Topological Features of Persistence Diagrams 持久性图拓扑特征的贝叶斯估计

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-01-01 DOI: 10.1214/22-ba1341

Asael Fabian Mart'inez

引用次数: 0