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Fast Bayesian Inference for Spatial Mean-Parameterized Conway–Maxwell–Poisson Models 空间均值参数化康威-麦克斯韦-泊松模型的快速贝叶斯推理
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-08-21 DOI: 10.1080/10618600.2024.2394460
Bokgyeong Kang, John Hughes, Murali Haran
{"title":"Fast Bayesian Inference for Spatial Mean-Parameterized Conway–Maxwell–Poisson Models","authors":"Bokgyeong Kang, John Hughes, Murali Haran","doi":"10.1080/10618600.2024.2394460","DOIUrl":"https://doi.org/10.1080/10618600.2024.2394460","url":null,"abstract":"Count data with complex features arise in many disciplines, including ecology, agriculture, criminology, medicine, and public health. Zero inflation, spatial dependence, and non-equidispersion are ...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"7 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142101054","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
Beyond time-homogeneity for continuous-time multistate Markov models 超越连续时间多态马尔可夫模型的时间同质性
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-08-08 DOI: 10.1080/10618600.2024.2388609
Emmett B. Kendall, Jonathan P. Williams, Gudmund H. Hermansen, Frederic Bois, Vo Hong Thanh
{"title":"Beyond time-homogeneity for continuous-time multistate Markov models","authors":"Emmett B. Kendall, Jonathan P. Williams, Gudmund H. Hermansen, Frederic Bois, Vo Hong Thanh","doi":"10.1080/10618600.2024.2388609","DOIUrl":"https://doi.org/10.1080/10618600.2024.2388609","url":null,"abstract":"Multistate Markov models are a canonical parametric approach for data modeling of observed or latent stochastic processes supported on a finite state space. Continuous-time Markov processes describ...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"12 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141909305","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
Degrees of Freedom: Search Cost and Self-consistency 自由度：搜索成本与自洽性
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-08-08 DOI: 10.1080/10618600.2024.2388545
Lijun Wang, Hongyu Zhao, Xiaodan Fan
{"title":"Degrees of Freedom: Search Cost and Self-consistency","authors":"Lijun Wang, Hongyu Zhao, Xiaodan Fan","doi":"10.1080/10618600.2024.2388545","DOIUrl":"https://doi.org/10.1080/10618600.2024.2388545","url":null,"abstract":"Model degrees of freedom ( df ) is a fundamental concept in statistics because it quantifies the flexibility of a fitting procedure and is indispensable in model selection. To investigate the gap b...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"14 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141915026","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
Scalable Estimation for Structured Additive Distributional Regression 结构化附加分布回归的可扩展估计
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-08-08 DOI: 10.1080/10618600.2024.2388604
Nikolaus Umlauf, Johannes Seiler, Mattias Wetscher, Thorsten Simon, Stefan Lang, Nadja Klein
{"title":"Scalable Estimation for Structured Additive Distributional Regression","authors":"Nikolaus Umlauf, Johannes Seiler, Mattias Wetscher, Thorsten Simon, Stefan Lang, Nadja Klein","doi":"10.1080/10618600.2024.2388604","DOIUrl":"https://doi.org/10.1080/10618600.2024.2388604","url":null,"abstract":"Obtaining probabilistic models is of high relevance in many recent applications. However, estimation of such distributional models with very large datasets remains a difficult task. In particular, ...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"24 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130828","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
Using rejection sampling probability of acceptance as a measure of independence 用拒绝抽样的接受概率来衡量独立性
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-08-06 DOI: 10.1080/10618600.2024.2388544
Markku Kuismin
{"title":"Using rejection sampling probability of acceptance as a measure of independence","authors":"Markku Kuismin","doi":"10.1080/10618600.2024.2388544","DOIUrl":"https://doi.org/10.1080/10618600.2024.2388544","url":null,"abstract":"This paper proposes a new association statistic for determining whether random variables are statistically independent. The proposed association statistic can also be used to examine the strength o...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"40 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141899847","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
Blocked Gibbs sampler for hierarchical Dirichlet processes 分层迪里希勒过程的阻塞吉布斯采样器
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-08-05 DOI: 10.1080/10618600.2024.2388543
Snigdha Das, Yabo Niu, Yang Ni, Bani K. Mallick, Debdeep Pati
{"title":"Blocked Gibbs sampler for hierarchical Dirichlet processes","authors":"Snigdha Das, Yabo Niu, Yang Ni, Bani K. Mallick, Debdeep Pati","doi":"10.1080/10618600.2024.2388543","DOIUrl":"https://doi.org/10.1080/10618600.2024.2388543","url":null,"abstract":"Posterior computation in hierarchical Dirichlet process (HDP) mixture models is an active area of research in nonparametric Bayes inference of grouped data. Existing literature almost exclusively f...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"300 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141899851","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
Augmentation Samplers for Multinomial Probit Bayesian Additive Regression Trees 多项式概率贝叶斯加性回归树的增量取样器
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-08-05 DOI: 10.1080/10618600.2024.2388605
Yizhen Xu, Joseph Hogan, Michael Daniels, Rami Kantor, Ann Mwangi
{"title":"Augmentation Samplers for Multinomial Probit Bayesian Additive Regression Trees","authors":"Yizhen Xu, Joseph Hogan, Michael Daniels, Rami Kantor, Ann Mwangi","doi":"10.1080/10618600.2024.2388605","DOIUrl":"https://doi.org/10.1080/10618600.2024.2388605","url":null,"abstract":"The multinomial probit (MNP) (Imai and van Dyk, 2005) framework is based on a multivariate Gaussian latent structure, allowing for natural extensions to multilevel modeling. Unlike multinomial logi...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"158 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141899848","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
Using early rejection Markov chain Monte Carlo and Gaussian processes to accelerate ABC methods 利用早期拒绝马尔科夫链蒙特卡洛和高斯过程加速 ABC 方法
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-07-15 DOI: 10.1080/10618600.2024.2379349
Xuefei Cao, Shijia Wang, Yongdao Zhou
{"title":"Using early rejection Markov chain Monte Carlo and Gaussian processes to accelerate ABC methods","authors":"Xuefei Cao, Shijia Wang, Yongdao Zhou","doi":"10.1080/10618600.2024.2379349","DOIUrl":"https://doi.org/10.1080/10618600.2024.2379349","url":null,"abstract":"Approximate Bayesian computation (ABC) is a class of Bayesian inference algorithms that targets problems with intractable or unavailable likelihood functions. It uses synthetic data drawn from the ...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"81 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730633","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
Computational methods for fast Bayesian model assessment via calibrated posterior p-values 通过校准后p值快速评估贝叶斯模型的计算方法
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-07-11 DOI: 10.1080/10618600.2024.2374585
Sally Paganin, Perry de Valpine
{"title":"Computational methods for fast Bayesian model assessment via calibrated posterior p-values","authors":"Sally Paganin, Perry de Valpine","doi":"10.1080/10618600.2024.2374585","DOIUrl":"https://doi.org/10.1080/10618600.2024.2374585","url":null,"abstract":"Posterior predictive p-values (ppps) have become popular tools for Bayesian model assessment, being general-purpose and easy to use. However, interpretation can be difficult because their distribut...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"18 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597631","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
Fast Computer Model Calibration using Annealed and Transformed Variational Inference 利用退火和变换变量推理进行快速计算机模型校准
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-07-08 DOI: 10.1080/10618600.2024.2374962
Dongkyu Derek Cho, Won Chang, Jaewoo Park
{"title":"Fast Computer Model Calibration using Annealed and Transformed Variational Inference","authors":"Dongkyu Derek Cho, Won Chang, Jaewoo Park","doi":"10.1080/10618600.2024.2374962","DOIUrl":"https://doi.org/10.1080/10618600.2024.2374962","url":null,"abstract":"Computer models play a crucial role in numerous scientific and engineering domains. To ensure the accuracy of simulations, it is essential to properly calibrate the input parameters of these models...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"28 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141584476","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
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