응용통계연구 (The Korean Journal of Applied Statistics)

  • 제34권1호
  • /
  • Pages.9-23
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  • 2021
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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약물동태학 모형에 대한 변분 베이즈 방법

A variational Bayes method for pharmacokinetic model

  • 박선 (전북대학교 통계학과) ;
  • 조성일 (인하대학교 통계학과) ;
  • 이우주 (서울대학교 보건대학원)
  • Parka, Sun (Department of Statistics, Jeonbuk National University) ;
  • Jo, Seongil (Department of Statistics, Inha University) ;
  • Lee, Woojoo (Graduate School of Public Health, Seoul National University)
  • 투고 : 2020.10.05
  • 심사 : 2020.12.07
  • 발행 : 2021.02.28
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 평균장 방법(mean-field methods)을 기반으로 사후 분포(posterior distribution)를 근사하는 방법인 변분 베이즈 방법(variational Bayes methods)에 대해 소개한다. 특히, 모수들을 실수공간으로 변환 후의 결합 사후분포를 가우시안 분포(Gaussian distribution)들의 곱(product)으로 근사하는 방법인 자동 미분 변분 추론(automatic differentiation variational inference)방법에 대해 자세히 소개하고, 환자에게 약물을 투여한 후 시간에 따라 약물의 흐름을 파악하는 연구인 약물동태학 모형(pharmacokinetic models)에 적용한다. 소개된 변분 베이즈 방법을 이용하여 자료분석을 실시하고 마코프 체인 몬테 카를로(Markov chain Monte Carlo)방법을 기초로한 자료분석의 결과와 비교한다. 알고리즘의 구현은 Stan을 이용한다.

In the following paper we introduce a variational Bayes method that approximates posterior distributions with mean-field method. In particular, we introduce automatic differentiation variation inference (ADVI), which approximates joint posterior distributions using the product of Gaussian distributions after transforming parameters into real coordinate space, and then apply it to pharmacokinetic models that are models for the study of the time course of drug absorption, distribution, metabolism and excretion. We analyze real data sets using ADVI and compare the results with those based on Markov chain Monte Carlo. We implement the algorithms using Stan.

키워드

과제정보

조성일의 연구는 2020년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임 (NRF-2020R1C1C1A01013338).

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