Modularized Bayesian analyses and cutting feedback in likelihood-free inference

Atlanta Chakraborty; David J. Nott; Christopher C. DrovandiDavid T. FrazierScott A. Sisson

首页> 外文期刊>Statistics and computing >Modularized Bayesian analyses and cutting feedback in likelihood-free inference

【24h】

Modularized Bayesian analyses and cutting feedback in likelihood-free inference

机译：Modularized Bayesian analyses and cutting feedback in likelihood-free inference

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

Abstract There has been much recent interest in modifying Bayesian inference for misspecified models so that it is useful for specific purposes. One popular modified Bayesian inference method is “cutting feedback” which can be used when the model consists of a number of coupled modules, with only some of the modules being misspecified. Cutting feedback methods represent the full posterior distribution in terms of conditional and sequential components, and then modify some terms in such a representation based on the modular structure for specification or computation of a modified posterior distribution. The main goal of this is to avoid contamination of inferences for parameters of interest by misspecified modules. Computation for cut posterior distributions is challenging, and here we consider cutting feedback for likelihood-free inference based on Gaussian mixture approximations to the joint distribution of parameters and data summary statistics. We exploit the fact that marginal and conditional distributions of a Gaussian mixture are Gaussian mixtures to give explicit approximations to marginal or conditional posterior distributions so that we can easily approximate cut posterior analyses. The mixture approach allows repeated approximation of posterior distributions for different data based on a single mixture fit. This is important for model checks which aid in the decision of whether to “cut”. A semi-modular approach to likelihood-free inference where feedback is partially cut is also developed. The benefits of the method are illustrated on two challenging examples, a collective cell spreading model and a continuous time model for asset returns with jumps.

著录项

来源
《Statistics and computing》 |2023年第1期|1.1-1.17|共17页
作者
Atlanta Chakraborty; David J. Nott; Christopher C. DrovandiDavid T. FrazierScott A. Sisson;
展开▼
作者单位

National University of Singapore;

National University of Singapore||National University of Singapore;

Queensland University of TechnologyMonash UniversityUniversity of New South Wales;

展开▼
收录信息
原文格式 PDF
正文语种英语
中图分类
关键词
Approximate Bayesian computation; Model misspecification; Modularization; Semi-modular inference; Synthetic likelihood;

Modularized Bayesian analyses and cutting feedback in likelihood-free inference

摘要

著录项

相关主题

期刊订阅