An Automated Prior Robustness Analysis in Bayesian Model Comparison

J. Chan, Liana Jacobi, Dan Zhu
{"title":"An Automated Prior Robustness Analysis in Bayesian Model Comparison","authors":"J. Chan, Liana Jacobi, Dan Zhu","doi":"10.2139/ssrn.3409549","DOIUrl":null,"url":null,"abstract":"The marginal likelihood is the gold standard for Bayesian model comparison although it is well-known that the value of marginal likelihood could be sensitive to the choice of prior hyperparameters. Most models require computationally intense simulation-based methods to evaluate the typically high-dimensional integral of the marginal likelihood expression. Hence, despite the recognition that prior sensitivity analysis is important in this context, it is rarely done in practice. In this paper we develop efficient and feasible methods to compute the sensitivities of marginal likelihood, obtained via two common simulation-based methods, with respect to any prior hyperparameter alongside the MCMC estimation algorithm. Our approach builds on Automatic Differentiation (AD), which has only recently been introduced to the more computationally intensive setting of Markov chain Monte Carlo simulation. We illustrate our approach with two empirical applications in the context of widely used multivariate time series models.","PeriodicalId":191102,"journal":{"name":"ERN: Time-Series Models (Multiple) (Topic)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Time-Series Models (Multiple) (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3409549","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

The marginal likelihood is the gold standard for Bayesian model comparison although it is well-known that the value of marginal likelihood could be sensitive to the choice of prior hyperparameters. Most models require computationally intense simulation-based methods to evaluate the typically high-dimensional integral of the marginal likelihood expression. Hence, despite the recognition that prior sensitivity analysis is important in this context, it is rarely done in practice. In this paper we develop efficient and feasible methods to compute the sensitivities of marginal likelihood, obtained via two common simulation-based methods, with respect to any prior hyperparameter alongside the MCMC estimation algorithm. Our approach builds on Automatic Differentiation (AD), which has only recently been introduced to the more computationally intensive setting of Markov chain Monte Carlo simulation. We illustrate our approach with two empirical applications in the context of widely used multivariate time series models.
贝叶斯模型比较中的自动先验鲁棒性分析
边际似然是贝叶斯模型比较的金标准,尽管众所周知,边际似然的值可能对先验超参数的选择很敏感。大多数模型需要基于计算强度模拟的方法来评估边际似然表达式的典型高维积分。因此,尽管认识到先验敏感性分析在这种情况下很重要,但在实践中很少这样做。在本文中,我们开发了有效和可行的方法来计算边际似然的灵敏度,通过两种常见的基于模拟的方法获得,相对于任何先验超参数和MCMC估计算法。我们的方法建立在自动微分(AD)的基础上,它最近才被引入到马尔可夫链蒙特卡罗模拟的计算密集型设置中。在广泛使用的多变量时间序列模型的背景下,我们用两个经验应用来说明我们的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信