Korean Journal of Psychology : General

理쒓렐샇 寃깋

Korean Journal of Psychology : General - Vol. 40 , No. 4

[ Article ]
The Korean Journal of Psychology: General - Vol. 40, No. 4, pp. 567-596
ISSN: 1229-067X (Print)
Print publication date 25 Dec 2021
Received 25 Oct 2021 Accepted 06 Nov 2021
DOI: https://doi.org/10.22257/kjp.2021.12.40.4.567

베이지안 분석에서 사전분포의 이해와 적용
이지윤 ; 김수영
이화여자대학교 심리학과

Understanding and applying prior distributions in Bayesian analyses
Ji-Yoon Lee ; Su-Young Kim
Department of Psychology, Ewha Womans University
Correspondence to : 김수영, 이화여자대학교 심리학과, 서울시 서대문구 이화여대길 52 Tel: 02-3277-3792, E-mail: suyoung.kim@ewha.ac.kr

Funding Information ▼

초록

최근 베이지안 추정 방법이 사회과학 분야에서 많은 관심을 받고 있다. 베이지안 방법에는 연구자의 배경지식을 추정에 반영할 수 있는 사전분포라는 특별한 요소가 있으며, 이를 어떻게 지정하는지가 추정 전반에 영향을 미친다. 사전분포는 베이지안 분석에서 가장 중요한 요소임에도 불구하고, 사전분포를 이해하고 적절히 지정하기 위해 참고할 수 있는 방법론적 연구는 부족한 상황이다. 본 연구는 사전분포의 중요성과 사전분포 지정에 대한 전반적인 내용을 다룬다. 먼저, 연구자가 사전분포를 직접 지정하지 않는, 즉 프로그램이 제공하는 디폴트 사전분포 방법을 알아본다. 자주 사용되는 프로그램들의 디폴트 사전분포를 알아봄과 더불어 디폴트 사전분포의 알려진 문제점도 확인한다. 다음으로는 연구자가 사전분포를 직접 지정하는 방법에 대해 다룬다. 직접 지정할 수 있는 사전분포에는 무정보 사전분포와 정보 사전분포가 있으며, 어떤 사전분포를 이용할지는 모수에 대한 사전 정보의 유무에 따라 결정된다. 무정보 사전분포의 필요성과 이를 지정할 때 참고할 수 있도록 제안된 방법을 다루고, 정보 사전분포를 지정할 때 참고할 수 있는 연구들을 제공하며, 여러 연구의 기준을 종합해 연구자의 정보성 선택에 참고할 수 있는 기준을 탐색한다. 이후 본문에서 논의한 방법들을 적용한 자료 예시를 통해 실질적 도움을 제공하고자 하였으며, 마지막으로 본 연구의 의의와 한계에 대해 논의한다.

Abstract

The Bayesian estimation method has recently received a lot of attention in the social sciences. The Bayesian method has a special factor of prior distribution that can reflect researchers’ background knowledge in the estimation process. The specification of the prior distribution affects the overall estimation. Despite prior distribution being the most important factor in Bayesian analysis, there is a lack of methodological research for understanding and appropriately specifying the prior distribution. Therefore, the present study tries to help researchers to apply the prior distribution to their estimation by addressing the importance of the prior distribution and the overall content of the prior specification. First, we explore the method that researchers do not directly specify the prior distribution. This method means selecting the default prior distribution automatically provided by the program, and if you want to use this option, you must know exactly what kind of the default prior distribution is actually provided. For this, we discuss the default priors of frequently used programs, as well as the known problem of the default priors. Second, we address the method that researchers do specify the prior distribution by themseleves. The prior distributions that can be directly specified include noninformative prior distributions and informative prior distributions. Which prior distribution to use is determined by the presence of prior information on parameters. This study deals with the necessity of noninformative prior distributions and the proposed method when specifying them, provides studies that can be referenced when specifying informative prior distributions, and explores criteria that can be referenced for the select of informativeness by synthesizing the criteria across many studies. We provide practical help through data examples applying the methods discussed in the text, and finally discuss the significance and limitations of the present study.


Keywords: Bayesian method, prior distribution, default priors, non-informative priors, informative priors
키워드: 베이지안 방법, 사전분포, 디폴트 사전분포, 무정보 사전분포, 정보 사전분포

Acknowledgments

이 논문은 2019년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2019S1A5A2A03041362).


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