Bayesian Analysis最新文献

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

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

Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator. 利用泰晤士估计器从后验模拟中轻松计算边际似然。

IF 2.5 2区数学

Bayesian Analysis Pub Date : 2024-04-23 DOI: 10.1214/24-ba1422

Martin Metodiev, Marie Perrot-Dockès, Sarah Ouadah, Nicholas J Irons, Pierre Latouche, Adrian E Raftery

引用次数: 0

Fast Methods for Posterior Inference of Two-Group Normal-Normal Models. 两组正态模型后验推理的快速方法

IF 2.5 2区数学

Bayesian Analysis Pub Date : 2023-09-01 Epub Date: 2022-09-09 DOI: 10.1214/22-ba1329

Philip Greengard, Jeremy Hoskins, Charles C Margossian, Jonah Gabry, Andrew Gelman, Aki Vehtari

{"title":"Fast Methods for Posterior Inference of Two-Group Normal-Normal Models.","authors":"Philip Greengard, Jeremy Hoskins, Charles C Margossian, Jonah Gabry, Andrew Gelman, Aki Vehtari","doi":"10.1214/22-ba1329","DOIUrl":"10.1214/22-ba1329","url":null,"abstract":"We describe a class of algorithms for evaluating posterior moments of certain Bayesian linear regression models with a normal likelihood and a normal prior on the regression coefficients. The proposed methods can be used for hierarchical mixed effects models with partial pooling over one group of predictors, as well as random effects models with partial pooling over two groups of predictors. We demonstrate the performance of the methods on two applications, one involving U.S. opinion polls and one involving the modeling of COVID-19 outbreaks in Israel using survey data. The algorithms involve analytical marginalization of regression coefficients followed by numerical integration of the remaining low-dimensional density. The dominant cost of the algorithms is an eigendecomposition computed once for each value of the outside parameter of integration. Our approach drastically reduces run times compared to state-of-the-art Markov chain Monte Carlo (MCMC) algorithms. The latter, in addition to being computationally expensive, can also be difficult to tune when applied to hierarchical models.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":"889-907"},"PeriodicalIF":2.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12442500/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46750531","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

Reproducible Model Selection Using Bagged Posteriors. 利用袋装后验进行可重复性模型选择。

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2023-03-01 DOI: 10.1214/21-ba1301

Jonathan H Huggins, Jeffrey W Miller

{"title":"Reproducible Model Selection Using Bagged Posteriors.","authors":"Jonathan H Huggins, Jeffrey W Miller","doi":"10.1214/21-ba1301","DOIUrl":"https://doi.org/10.1214/21-ba1301","url":null,"abstract":"Bayesian model selection is premised on the assumption that the data are generated from one of the postulated models. However, in many applications, all of these models are incorrect (that is, there is misspecification). When the models are misspecified, two or more models can provide a nearly equally good fit to the data, in which case Bayesian model selection can be highly unstable, potentially leading to self-contradictory findings. To remedy this instability, we propose to use bagging on the posterior distribution (\"BayesBag\") - that is, to average the posterior model probabilities over many bootstrapped datasets. We provide theoretical results characterizing the asymptotic behavior of the posterior and the bagged posterior in the (misspecified) model selection setting. We empirically assess the BayesBag approach on synthetic and real-world data in (i) feature selection for linear regression and (ii) phylogenetic tree reconstruction. Our theory and experiments show that, when all models are misspecified, BayesBag (a) provides greater reproducibility and (b) places posterior mass on optimal models more reliably, compared to the usual Bayesian posterior; on the other hand, under correct specification, BayesBag is slightly more conservative than the usual posterior, in the sense that BayesBag posterior probabilities tend to be slightly farther from the extremes of zero and one. Overall, our results demonstrate that BayesBag provides an easy-to-use and widely applicable approach that improves upon Bayesian model selection by making it more stable and reproducible.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":"18 1","pages":"79-104"},"PeriodicalIF":4.4,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838736/pdf/nihms-1796997.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9229540","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: 10