Bayesian Analysis最新文献_第8页

The Attraction Indian Buffet Distribution 吸引力印度自助餐分布

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-06-09 DOI: 10.1214/21-ba1279

R. Warr, D. B. Dahl, Jeremy M. Meyer, Arthur Lui

{"title":"The Attraction Indian Buffet Distribution","authors":"R. Warr, D. B. Dahl, Jeremy M. Meyer, Arthur Lui","doi":"10.1214/21-ba1279","DOIUrl":"https://doi.org/10.1214/21-ba1279","url":null,"abstract":"We propose the attraction Indian buffet distribution (AIBD), a distribution for binary feature matrices influenced by pairwise similarity information. Binary feature matrices are used in Bayesian models to uncover latent variables (i.e., features) that explain observed data. The Indian buffet process (IBP) is a popular exchangeable prior distribution for latent feature matrices. In the presence of additional information, however, the exchangeability assumption is not reasonable or desirable. The AIBD can incorporate pairwise similarity information, yet it preserves many properties of the IBP, including the distribution of the total number of features. Thus, much of the interpretation and intuition that one has for the IBP directly carries over to the AIBD. A temperature parameter controls the degree to which the similarity information affects feature-sharing between observations. Unlike other nonexchangeable distributions for feature allocations, the probability mass function of the AIBD has a tractable normalizing constant, making posterior inference on hyperparameters straight-forward using standard MCMC methods. A novel posterior sampling algorithm is proposed for the IBP and the AIBD. We demonstrate the feasibility of the AIBD as a prior distribution in feature allocation models and compare the performance of competing methods in simulations and an application.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49171208","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

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-06-06 DOI: 10.1214/22-ba1326

Andrea Cremaschi, Raffele Argiento, M. Iorio, S. Cai, Y. Chong, M. Meaney, Michelle Z L Kee

{"title":"Seemingly Unrelated Multi-State Processes: A Bayesian Semiparametric Approach","authors":"Andrea Cremaschi, Raffele Argiento, M. Iorio, S. Cai, Y. Chong, M. Meaney, Michelle Z L Kee","doi":"10.1214/22-ba1326","DOIUrl":"https://doi.org/10.1214/22-ba1326","url":null,"abstract":"Many applications in medical statistics as well as in other fields can be described by transitions between multiple states (e.g. from health to disease) experienced by individuals over time. In this context, multi-state models are a popular statistical technique, in particular when the exact transition times are not observed. The key quantities of interest are the transition rates, capturing the instantaneous risk of moving from one state to another. The main contribution of this work is to propose a joint semiparametric model for several possibly related multi-state processes (Seemingly Unrelated Multi-State, SUMS, processes), assuming a Markov structure for the transitions over time. The dependence between different processes is captured by specifying a joint random effect distribution on the transition rates of each process. We assume a flexible random effect distribution, which allows for clustering of the individuals, overdispersion and outliers. Moreover, we employ a graph structure to describe the dependence among processes, exploiting tools from the Gaussian Graphical model literature. It is also possible to include covariate effects. We use our approach to model disease progression in mental health. Posterior inference is performed through a specially devised MCMC algorithm.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46358294","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

Bayesian Multiple Changepoint Detection for Stochastic Models in Continuous Time 连续时间随机模型的贝叶斯多变化点检测

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-06-01 DOI: 10.1214/20-ba1218

Lu Shaochuan

引用次数: 6

A Bayesian Approach for Partial Gaussian Graphical Models With Sparsity 具有稀疏性的部分高斯图模型的贝叶斯方法

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-05-23 DOI: 10.1214/22-ba1315

Eunice Okome Obiang, Pascal J'ez'equel, Fr'ed'eric Proia

引用次数: 1

A Metropolized Adaptive Subspace Algorithm for High-Dimensional Bayesian Variable Selection 一种高维贝叶斯变量选择的大都市自适应子空间算法

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-05-03 DOI: 10.1214/22-BA1351

C. Staerk, M. Kateri, I. Ntzoufras

引用次数: 0

Inverse Bayesian Optimization: Learning Human Acquisition Functions in an Exploration vs Exploitation Search Task 逆贝叶斯优化:在探索与开发搜索任务中学习人类习得函数

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-04-16 DOI: 10.1214/21-BA1303

N. Sandholtz, Yohsuke R. Miyamoto, L. Bornn, Maurice A. Smith

引用次数: 2

Bayesian Functional Principal Components Analysis via Variational Message Passing with Multilevel Extensions 基于多级扩展变分消息传递的贝叶斯函数主成分分析

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-04-01 DOI: 10.1214/23-ba1393

T. Nolan, J. Goldsmith, D. Ruppert

{"title":"Bayesian Functional Principal Components Analysis via Variational Message Passing with Multilevel Extensions","authors":"T. Nolan, J. Goldsmith, D. Ruppert","doi":"10.1214/23-ba1393","DOIUrl":"https://doi.org/10.1214/23-ba1393","url":null,"abstract":"Functional principal components analysis is a popular tool for inference on functional data. Standard approaches rely on an eigendecomposition of a smoothed covariance surface in order to extract the orthonormal functions representing the major modes of variation. This approach can be a computationally intensive procedure, especially in the presence of large datasets with irregular observations. In this article, we develop a Bayesian approach, which aims to determine the Karhunen-Lo`eve decomposition directly without the need to smooth and estimate a covariance surface. More specifically, we develop a variational Bayesian algorithm via message passing over a factor graph, which is more commonly referred to as variational message passing. Message passing algorithms are a powerful tool for compartmentalizing the algebra and coding required for inference in hierarchical statistical models. Recently, there has been much focus on formulating variational inference algorithms in the message passing framework because it removes the need for rederiving approximate posterior density functions if there is a change to the model. Instead, model changes are handled by changing specific computational units, known as fragments, within the factor graph. We extend the notion of variational message passing to functional principal components analysis. Indeed, this is the first article to address a functional data model via variational message passing. Our approach introduces two new fragments that are necessary for Bayesian functional principal components analysis. We present the computational details, a set of simulations for assessing accuracy and speed and an application to United States temperature data.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49573018","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: 3

Bayesian Optimal Experimental Design for Inferring Causal Structure 推理因果结构的贝叶斯最优实验设计

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-03-28 DOI: 10.1214/22-ba1335

M. Zemplenyi, Jeffrey W. Miller

引用次数: 3

Centered Partition Processes: Informative Priors for Clustering (with Discussion). 中心分区过程:聚类的信息先验(与讨论)。

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-03-01 DOI: 10.1214/20-BA1197

Sally Paganin, Amy H Herring, Andrew F Olshan, David B Dunson

{"title":"Centered Partition Processes: Informative Priors for Clustering (with Discussion).","authors":"Sally Paganin, Amy H Herring, Andrew F Olshan, David B Dunson","doi":"10.1214/20-BA1197","DOIUrl":"https://doi.org/10.1214/20-BA1197","url":null,"abstract":"There is a very rich literature proposing Bayesian approaches for clustering starting with a prior probability distribution on partitions. Most approaches assume exchangeability, leading to simple representations in terms of Exchangeable Partition Probability Functions (EPPF). Gibbs-type priors encompass a broad class of such cases, including Dirichlet and Pitman-Yor processes. Even though there have been some proposals to relax the exchangeability assumption, allowing covariate-dependence and partial exchangeability, limited consideration has been given on how to include concrete prior knowledge on the partition. For example, we are motivated by an epidemiological application, in which we wish to cluster birth defects into groups and we have prior knowledge of an initial clustering provided by experts. As a general approach for including such prior knowledge, we propose a Centered Partition (CP) process that modifies the EPPF to favor partitions close to an initial one. Some properties of the CP prior are described, a general algorithm for posterior computation is developed, and we illustrate the methodology through simulation examples and an application to the motivating epidemiology study of birth defects.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364237/pdf/nihms-1815470.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10689706","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: 13

A Phase I-II Basket Trial Design to Optimize Dose-Schedule Regimes Based on Delayed Outcomes. 基于延迟结果优化剂量方案的I-II期篮子试验设计

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-03-01 Epub Date: 2020-03-28 DOI: 10.1214/20-ba1205

Ruitao Lin, Peter F Thall, Ying Yuan

{"title":"A Phase I-II Basket Trial Design to Optimize Dose-Schedule Regimes Based on Delayed Outcomes.","authors":"Ruitao Lin, Peter F Thall, Ying Yuan","doi":"10.1214/20-ba1205","DOIUrl":"https://doi.org/10.1214/20-ba1205","url":null,"abstract":"This paper proposes a Bayesian adaptive basket trial design to optimize the dose-schedule regimes of an experimental agent within disease subtypes, called \"baskets\", for phase I-II clinical trials based on late-onset efficacy and toxicity. To characterize the association among the baskets and regimes, a Bayesian hierarchical model is assumed that includes a heterogeneity parameter, adaptively updated during the trial, that quantifies information shared across baskets. To account for late-onset outcomes when doing sequential decision making, unobserved outcomes are treated as missing values and imputed by exploiting early biomarker and low-grade toxicity information. Elicited joint utilities of efficacy and toxicity are used for decision making. Patients are randomized adaptively to regimes while accounting for baskets, with randomization probabilities proportional to the posterior probability of achieving maximum utility. Simulations are presented to assess the design's robustness and ability to identify optimal dose-schedule regimes within disease subtypes, and to compare it to a simplified design that treats the subtypes independently.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277108/pdf/nihms-1714036.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39191577","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: 8