Bayesian Analysis最新文献

Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking. 基于预测叠加的时空非高斯数据贝叶斯推理。

IF 2.5 2区数学

Bayesian Analysis Pub Date : 2026-01-06 DOI: 10.1214/25-ba1582

Soumyakanti Pan, Lu Zhang, Jonathan R Bradley, Sudipto Banerjee

{"title":"Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking.","authors":"Soumyakanti Pan, Lu Zhang, Jonathan R Bradley, Sudipto Banerjee","doi":"10.1214/25-ba1582","DOIUrl":"https://doi.org/10.1214/25-ba1582","url":null,"abstract":"Analysing non-Gaussian spatial-temporal data requires introducing spatial as well as temporal dependence in generalised linear models through the link function of an exponential family distribution. Unlike in Gaussian likelihoods, inference is considerably encumbered by the inability to analytically integrate out the random effects and reduce the dimension of the parameter space. Iterative estimation algorithms struggle to converge due to the presence of weakly identified parameters. We devise Bayesian inference using predictive stacking that assimilates inference from analytically tractable conditional posterior distributions. We achieve this by expanding upon the Diaconis-Ylvisaker family of conjugate priors and exploiting generalised conjugate multivariate (GCM) distribution theory for exponential families, which enables exact sampling from analytically available posterior distributions conditional upon some process parameters. Subsequently, we assimilate inference over a range of values of these parameters using Bayesian predictive stacking. We evaluate inferential performance on simulated data, compare with full Bayesian inference using Markov chain Monte Carlo (MCMC) and apply our method to analyse spatially-temporally referenced avian count data from the North American Breeding Bird Survey database.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13105312/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789878","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

Logistic-Beta Processes for Dependent Random Probabilities with Beta Marginals. 具有Beta边际的相关随机概率的Logistic-Beta过程。

IF 2.5 2区数学

Bayesian Analysis Pub Date : 2025-12-01 Epub Date: 2025-06-27 DOI: 10.1214/25-ba1541

Changwoo J Lee, Alessandro Zito, Huiyan Sang, David B Dunson

引用次数: 0

Quantum Speedups for Multiproposal MCMC. 多提案MCMC的量子加速。

IF 2.5 2区数学

Bayesian Analysis Pub Date : 2025-08-18 DOI: 10.1214/25-ba1546

Chin-Yi Lin, Kuo-Chin Chen, Philippe Lemey, Marc A Suchard, Andrew J Holbrook, Min-Hsiu Hsieh

{"title":"Quantum Speedups for Multiproposal MCMC.","authors":"Chin-Yi Lin, Kuo-Chin Chen, Philippe Lemey, Marc A Suchard, Andrew J Holbrook, Min-Hsiu Hsieh","doi":"10.1214/25-ba1546","DOIUrl":"10.1214/25-ba1546","url":null,"abstract":"Multiproposal Markov chain Monte Carlo (MCMC) algorithms choose from multiple proposals to generate their next chain step in order to sample from challenging target distributions more efficiently. However, on classical machines, these algorithms require <math><mi>𝒪</mi> <mo>(</mo> <mi>P</mi> <mo>)</mo></math> target evaluations for each Markov chain step when choosing from <math><mi>P</mi></math> proposals. Recent work demonstrates the possibility of quadratic quantum speedups for one such multiproposal MCMC algorithm. After generating <math><mi>P</mi></math> proposals, this quantum parallel MCMC (QPMCMC) algorithm requires only <math><mi>𝒪</mi> <mo>(</mo> <msqrt><mi>P</mi></msqrt> <mo>)</mo></math> target evaluations at each step, outperforming its classical counterpart. However, generating <math><mi>P</mi></math> proposals using classical computers still requires <math><mi>𝒪</mi> <mo>(</mo> <mi>P</mi> <mo>)</mo></math> time complexity, resulting in the overall complexity of QPMCMC remaining <math><mi>𝒪</mi> <mo>(</mo> <mi>P</mi> <mo>)</mo></math> . Here, we present a new, faster quantum multiproposal MCMC strategy, QPMCMC2. With a specially designed Tjelmeland distribution that generates proposals close to the input state, QPMCMC2 requires only <math><mi>𝒪</mi> <mo>(</mo> <mn>1</mn> <mo>)</mo></math> target evaluations and <math><mi>𝒪</mi> <mo>(</mo> <mtext>log</mtext> <mspace></mspace> <mi>P</mi> <mo>)</mo></math> qubits when computing over a large number of proposals <math><mi>P</mi></math> . Unlike its slower predecessor, the QPMCMC2 Markov kernel (1) maintains detailed balance exactly and (2) is fully explicit for a large class of graphical models. We demonstrate this flexibility by applying QPMCMC2 to novel Ising-type models built on bacterial evolutionary networks and obtain significant speedups for Bayesian ancestral trait reconstruction for 248 observed salmonella bacteria.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12456418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145139502","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

Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity. 基于仿真的贝叶斯计算校准检验：试验量形状灵敏度的选择。

IF 2.5 2区数学

Bayesian Analysis Pub Date : 2025-06-01 Epub Date: 2023-11-23 DOI: 10.1214/23-ba1404

Martin Modrák, Angie H Moon, Shinyoung Kim, Paul Bürkner, Niko Huurre, Kateřina Faltejsková, Andrew Gelman, Aki Vehtari

{"title":"Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity.","authors":"Martin Modrák, Angie H Moon, Shinyoung Kim, Paul Bürkner, Niko Huurre, Kateřina Faltejsková, Andrew Gelman, Aki Vehtari","doi":"10.1214/23-ba1404","DOIUrl":"10.1214/23-ba1404","url":null,"abstract":"Simulation-based calibration checking (SBC) is a practical method to validate computationally-derived posterior distributions or their approximations. In this paper, we introduce a new variant of SBC to alleviate several known problems. Our variant allows the user to in principle detect any possible issue with the posterior, while previously reported implementations could never detect large classes of problems including when the posterior is equal to the prior. This is made possible by including additional data-dependent test quantities when running SBC. We argue and demonstrate that the joint likelihood of the data is an especially useful test quantity. Some other types of test quantities and their theoretical and practical benefits are also investigated. We provide theoretical analysis of SBC, thereby providing a more complete understanding of the underlying statistical mechanisms. We also bring attention to a relatively common mistake in the literature and clarify the difference between SBC and checks based on the data-averaged posterior. We support our recommendations with numerical case studies on a multivariate normal example and a case study in implementing an ordered simplex data type for use with Hamiltonian Monte Carlo. The SBC variant introduced in this paper is implemented in the SBC R package.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":"20 2","pages":"461-488"},"PeriodicalIF":2.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12490788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234300","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

Nonparametric Bayes Differential Analysis of Multigroup DNA Methylation Data. 多组DNA甲基化数据的非参数贝叶斯差异分析。

IF 4.9 2区数学

Bayesian Analysis Pub Date : 2025-06-01 Epub Date: 2023-11-23 DOI: 10.1214/23-ba1407

Chiyu Gu, Veerabhadran Baladandayuthapani, Subharup Guha

{"title":"Nonparametric Bayes Differential Analysis of Multigroup DNA Methylation Data.","authors":"Chiyu Gu, Veerabhadran Baladandayuthapani, Subharup Guha","doi":"10.1214/23-ba1407","DOIUrl":"10.1214/23-ba1407","url":null,"abstract":"DNA methylation datasets in cancer studies are comprised of measurements on a large number of genomic locations called cytosine-phosphate-guanine (CpG) sites with complex correlation structures. A fundamental goal of these studies is the development of statistical techniques that can identify disease genomic signatures across multiple patient groups defined by different experimental or biological conditions. We propose BayesDiff, a nonparametric Bayesian approach for differential analysis relying on a novel class of first order mixture models called the Sticky Pitman-Yor process or two-restaurant two-cuisine franchise (2R2CF). The BayesDiff methodology flexibly utilizes information from all CpG sites or biomarker probes, adaptively accommodates any serial dependence due to the widely varying inter-probe distances, and makes posterior inferences about the differential genomic signature of patient groups. Using simulation studies, we demonstrate the effectiveness of the BayesDiff procedure relative to existing statistical techniques for differential DNA methylation. The methodology is applied to analyze a gastrointestinal (GI) cancer dataset exhibiting serial correlation and complex interaction patterns. The results support and complement known aspects of DNA methylation and gene association in upper GI cancers.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":"20 2","pages":"489-518"},"PeriodicalIF":4.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12094113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129658","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

Gridding and Parameter Expansion for Scalable Latent Gaussian Models of Spatial Multivariate Data. 空间多元数据可伸缩隐高斯模型的网格化和参数展开。

IF 2.5 2区数学

Bayesian Analysis Pub Date : 2025-03-12 DOI: 10.1214/25-BA1515

Michele Peruzzi, Sudipto Banerjee, David B Dunson, Andrew O Finley

{"title":"Gridding and Parameter Expansion for Scalable Latent Gaussian Models of Spatial Multivariate Data.","authors":"Michele Peruzzi, Sudipto Banerjee, David B Dunson, Andrew O Finley","doi":"10.1214/25-BA1515","DOIUrl":"10.1214/25-BA1515","url":null,"abstract":"Scalable spatial GPs for massive datasets can be built via sparse Directed Acyclic Graphs (DAGs) where a small number of directed edges is sufficient to flexibly characterize spatial dependence. The DAG can be used to devise fast algorithms for posterior sampling of the latent process, but these may exhibit pathological behavior in estimating covariance parameters. In this article, we introduce gridding and parameter expansion methods to improve the practical performance of MCMC algorithms in terms of effective sample size per unit time (ESS/s). Gridding is a model-based strategy that reduces the number of expensive operations necessary during MCMC on irregularly spaced data. Parameter expansion reduces dependence in posterior samples in spatial regression for high resolution data. These two strategies lead to computational gains in the big data settings on which we focus. We consider popular constructions of univariate spatial processes based on Matérn covariance functions and multivariate coregionalization models for Gaussian outcomes in extensive analyses of synthetic datasets comparing with alternative methods. We demonstrate effectiveness of our proposed methods in a forestry application using remotely sensed data from NASA's Goddard LiDAR, Hyper-Spectral, and Thermal imager (G-LiHT).","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12533814/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145330923","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

Exploiting Multivariate Network Meta-Analysis: A Calibrated Bayesian Composite Likelihood Inference. 利用多元网络元分析：校准贝叶斯复合似然推断。

IF 2.5 2区数学

Bayesian Analysis Pub Date : 2025-02-20 DOI: 10.1214/25-ba1511

Yifei Wang, Lifeng Lin, Yu-Lun Liu

{"title":"Exploiting Multivariate Network Meta-Analysis: A Calibrated Bayesian Composite Likelihood Inference.","authors":"Yifei Wang, Lifeng Lin, Yu-Lun Liu","doi":"10.1214/25-ba1511","DOIUrl":"10.1214/25-ba1511","url":null,"abstract":"Multivariate network meta-analysis has emerged as a powerful tool for evidence synthesis by incorporating multiple outcomes and treatments. Despite its advantages, this method comes with methodological challenges, such as the issue of unreported within-study correlations among treatments and outcomes, which can lead to biased estimates and misleading conclusions. In this paper, we propose a calibrated Bayesian composite likelihood approach to overcome this limitation. The proposed method eliminates the need for a fully specified likelihood function while allowing for the unavailability of within-study correlations among treatments and outcomes. Additionally, we developed a hybrid Gibbs sampler algorithm along with the Open-Faced Sandwich post-sampling adjustment to enable robust posterior inference. Through comprehensive simulation studies, we demonstrated that the proposed approach yields unbiased estimates while maintaining coverage probabilities close to the nominal levels. We implemented the proposed method to two real-world network meta-analysis datasets: one comparing treatment procedures for root coverage and the other comparing treatments for anemia in patients with chronic kidney disease.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453069/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132898","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

Causally Sound Priors for Binary Experiments. 二元实验的因果合理先验。

IF 2.5 2区数学

Bayesian Analysis Pub Date : 2025-01-28 DOI: 10.1214/25-BA1506

Nicholas J Irons, Carlos Cinelli

{"title":"Causally Sound Priors for Binary Experiments.","authors":"Nicholas J Irons, Carlos Cinelli","doi":"10.1214/25-BA1506","DOIUrl":"10.1214/25-BA1506","url":null,"abstract":"We introduce the BREASE framework for the Bayesian analysis of randomized controlled trials with binary treatment and outcome. Approaching the problem from a causal inference perspective, we propose parameterizing the likelihood in terms of the baseline risk, efficacy, and adverse side effects of the treatment, along with a flexible, yet intuitive and tractable jointly independent beta prior distribution on these parameters, which we show to be a generalization of the Dirichlet prior for the joint distribution of potential outcomes. Our approach has a number of desirable characteristics when compared to current mainstream alternatives: (i) it naturally induces prior dependence between expected outcomes in the treatment and control groups; (ii) as the baseline risk, efficacy and risk of adverse side effects are quantities commonly present in the clinicians' vocabulary, the hyperparameters of the prior are directly interpretable, thus facilitating the elicitation of prior knowledge and sensitivity analysis; and (iii) we provide analytical formulae for the marginal likelihood, Bayes factor, and other posterior quantities, as well as an exact posterior sampling algorithm and an accurate and fast data-augmented Gibbs sampler in cases where traditional MCMC fails. Empirical examples demonstrate the utility of our methods for estimation, hypothesis testing, and sensitivity analysis of treatment effects.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12416923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145031209","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

Bag of DAGs: Inferring Directional Dependence in Spatiotemporal Processes. DAGs袋：推断时空过程中的方向依赖性。

IF 2.5 2区数学

Bayesian Analysis Pub Date : 2024-11-11 DOI: 10.1214/24-ba1473

Bora Jin, Michele Peruzzi, David Dunson

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