Informative priors in Bayesian inference and computation

被引：2

作者：

Golchi, Shirin ^{[1
]}

机构：

[1] Simon Fraser Univ, Dept Stat & Actuarial Sci, Burnaby, BC, Canada

来源：

STATISTICAL ANALYSIS AND DATA MINING | 2019年 / 12卷 / 02期

基金：

美国国家科学基金会; 加拿大自然科学与工程研究理事会;

关键词：

aggregated relational data; constraints; differential equation models; particle discovery; sequential Monte Carlo; uncertainty; PEOPLE;

D O I：

10.1002/sam.11371

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The use of prior distributions is often a controversial topic in Bayesian inference. Informative priors are often avoided at all costs. However, when prior information is available, informative priors are appropriate means of introducing this information into the model. Furthermore, informative priors, when used properly and creatively, can provide solutions to computational issues and improve inference. Through 3 examples with different applications, we demonstrate the importance and utilities of informative priors in incorporating external information into the model and overcoming computational difficulties.

引用

页码：45 / 55

页数：11

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