Bayesian Analysis最新文献_第4页

Approximate Bayesian Inference Based on Expected Evaluation 基于期望评价的近似贝叶斯推理

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2023-01-01 DOI: 10.1214/23-ba1368

H. Hammer, M. Riegler, H. Tjelmeland

引用次数: 0

Generalized Geographically Weighted Regression Model within a Modularized Bayesian Framework. 模块化贝叶斯框架内的广义地理加权回归模型。

IF 4.9 2区数学

Bayesian Analysis Pub Date : 2023-01-01 DOI: 10.1214/22-BA1357

Yang Liu, Robert J B Goudie

引用次数: 0

Defining a Credible Interval Is Not Always Possible with “Point-Null” Priors: A Lesser-Known Correlate of the Jeffreys-Lindley Paradox 用“零点”先验定义可信区间并不总是可能的：Jeffrey-Lindley悖论的一个鲜为人知的相关性

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-09-30 DOI: 10.1214/23-ba1397

Harlan Campbell, P. Gustafson

引用次数: 2

Structure Induced by a Multiple Membership Transformation on the Conditional Autoregressive Model 条件自回归模型的多重隶属变换诱导的结构

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-08-13 DOI: 10.1214/23-ba1370

Marco Gramatica, S. Liverani, Peter Congdon

{"title":"Structure Induced by a Multiple Membership Transformation on the Conditional Autoregressive Model","authors":"Marco Gramatica, S. Liverani, Peter Congdon","doi":"10.1214/23-ba1370","DOIUrl":"https://doi.org/10.1214/23-ba1370","url":null,"abstract":"The objective of disease mapping is to model data aggregated at the areal level. In some contexts, however, (e.g. residential histories, general practitioner catchment areas) when data is arising from a variety of sources, not necessarily at the same spatial scale, it is possible to specify spatial random effects, or covariate effects, at the areal level, by using a multiple membership principle (MM) (Petrof et al. 2020, Gramatica et al. 2021). A weighted average of conditional autoregressive (CAR) spatial random effects embeds spatial information for a spatially-misaligned outcome and estimate relative risk for both frameworks (areas and memberships). In this paper we investigate the theoretical underpinnings of these application of the multiple membership principle to the CAR prior, in particular with regard to parameterisation, properness and identifiability. We carry out simulations involving different numbers of memberships as compared to number of areas and assess impact of this on estimating parameters of interest. Both analytical and simulation study results show under which conditions parameters of interest are identifiable, so that we can offer actionable recommendations to practitioners. Finally, we present the results of an application of the multiple membership model to diabetes prevalence data in South London, together with strategic implications for public health considerations","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2022-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47219745","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

Bayesian Data Augmentation for Partially Observed Stochastic Compartmental Models 部分观测随机区室模型的贝叶斯数据增强

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-06-17 DOI: 10.1214/23-ba1398

Shuying Wang, S. Walker

引用次数: 1

Consistent and Scalable Bayesian Joint Variable and Graph Selection for Disease Diagnosis Leveraging Functional Brain Network 基于功能脑网络的疾病诊断一致可扩展贝叶斯联合变量和图选择

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-03-14 DOI: 10.1214/23-ba1376

Xuan Cao, Kyoungjae Lee

引用次数: 0

Scalable Approximate Bayesian Computation for Growing Network Models via Extrapolated and Sampled Summaries. 通过外推和采样摘要为成长网络模型提供可扩展的近似贝叶斯计算。

IF 4.9 2区数学

Bayesian Analysis Pub Date : 2022-03-01 Epub Date: 2020-12-08 DOI: 10.1214/20-ba1248

Louis Raynal, Sixing Chen, Antonietta Mira, Jukka-Pekka Onnela

{"title":"Scalable Approximate Bayesian Computation for Growing Network Models via Extrapolated and Sampled Summaries.","authors":"Louis Raynal, Sixing Chen, Antonietta Mira, Jukka-Pekka Onnela","doi":"10.1214/20-ba1248","DOIUrl":"10.1214/20-ba1248","url":null,"abstract":"<p><p>Approximate Bayesian computation (ABC) is a simulation-based likelihood-free method applicable to both model selection and parameter estimation. ABC parameter estimation requires the ability to forward simulate datasets from a candidate model, but because the sizes of the observed and simulated datasets usually need to match, this can be computationally expensive. Additionally, since ABC inference is based on comparisons of summary statistics computed on the observed and simulated data, using computationally expensive summary statistics can lead to further losses in efficiency. ABC has recently been applied to the family of mechanistic network models, an area that has traditionally lacked tools for inference and model choice. Mechanistic models of network growth repeatedly add nodes to a network until it reaches the size of the observed network, which may be of the order of millions of nodes. With ABC, this process can quickly become computationally prohibitive due to the resource intensive nature of network simulations and evaluation of summary statistics. We propose two methodological developments to enable the use of ABC for inference in models for large growing networks. First, to save time needed for forward simulating model realizations, we propose a procedure to extrapolate (via both least squares and Gaussian processes) summary statistics from small to large networks. Second, to reduce computation time for evaluating summary statistics, we use sample-based rather than census-based summary statistics. We show that the ABC posterior obtained through this approach, which adds two additional layers of approximation to the standard ABC, is similar to a classic ABC posterior. Although we deal with growing network models, both extrapolated summaries and sampled summaries are expected to be relevant in other ABC settings where the data are generated incrementally.</p>","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9541316/pdf/nihms-1803183.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33497387","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 Inference on Hierarchical Nonlocal Priors in Generalized Linear Models 广义线性模型中层次非局部先验的贝叶斯推理

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2022-01-01 DOI: 10.1214/22-ba1350

Xuan Cao, Kyoungjae Lee

{"title":"Bayesian Inference on Hierarchical Nonlocal Priors in Generalized Linear Models","authors":"Xuan Cao, Kyoungjae Lee","doi":"10.1214/22-ba1350","DOIUrl":"https://doi.org/10.1214/22-ba1350","url":null,"abstract":"Variable selection methods with nonlocal priors have been widely studied in linear regression models, and their theoretical and empirical performances have been reported. However, the crucial model selection properties for hierarchical nonlocal priors in high-dimensional generalized linear regression have rarely been investigated. In this paper, we consider a hierarchical nonlocal prior for high-dimensional logistic regression models and investigate theoretical properties of the posterior distribution. Specifically, a product moment (pMOM) nonlocal prior is imposed over the regression coefficients with an Inverse-Gamma prior on the tuning parameter. Under standard regularity assumptions, we establish strong model selection consistency in a high-dimensional setting, where the number of covariates is allowed to increase at a subexponential rate with the sample size. We implement the Laplace approximation for computing the posterior probabilities, and a modified shotgun stochastic search procedure is suggested for efficiently exploring the model space. We demonstrate the validity of the proposed method through simulation studies and an RNA-sequencing dataset for stratifying disease risk.","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":"49089074","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

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":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":"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