The probability concepts mainly used for rainfall or flood frequency analysis in water resources planning are the frequentist viewpoint that defines the probability as the limit of relative frequency, and the unknown parameters in probability model are considered as fixed constant numbers. Thus the probability is objective and the parameters have fixed values so that it is very difficult to specify probabilistically the uncertianty of these parameters.
This study constructs the uncertainty evaluation model using Bayesian MCMC and Metropolis -Hastings algorithm for the uncertainty quantification of parameters of probability distribution in rainfall frequency analysis, and then from the application of Bayesian MCMC and Metropolis- Hastings algorithm, the statistical properties and uncertainty intervals of parameters of probability distribution can be quantified in the estimation of probability rainfall so that the basis for the framework configuration can be provided that can specify the uncertainty and risk in flood risk assessment and decision-making process.

Abstract
 1. 서 론
 2. 베이지안 해석
  2.1. 베이즈 정리
  2.2. 베이지안 마코프 연쇄 몬테카를로 기법
 3. 결과 및 고찰
  3.1. 적용유역 및 분석개요
  3.2. 확률밀도함수의 선정 및 우도함수
  3.3. 사전분포의 선정
  3.4. Metropolis-Hasting 알고리즘 적용 및 고찰
 4. 결 론
 참 고 문 헌

• 서영민(영남대학교 토목공학과) | Young Min Seo (Department of Civil Engineering, Yeungnam University)
• 박기범(동양대학교 철도토목과) | Ki Bum Park (Department of Railroad and Civil Engineering, Dongyang University) Corresponding Author