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      베이지안 기법의 발전 및 수자원 분야에의 적용 = Development of the Bayesian method and its application to the water resources field

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      다국어 초록 (Multilingual Abstract)

      The Bayesian method is a very useful statistical tool in various fields including water resources. Therefore, in this study, the background of the Bayesian statistics and its application to the water resources field are reviewed. First, the history of the Bayesian method from the birth to the present, and the achievements of Bayesian statisticians are summarized. Next, the derivation of the Bayes' theorem, which is the basis of the Bayesian method, is presented, and the roles of the three elements of the Bayes' theorem: priori distribution, likelihood function, and posteriori distribution are explained. In addition, the unique features and advantages of the Bayesian statistics are summarized. Finally, the cases in water resources where the Bayesian method is applied are summarized by dividing them into several categories. With a prevalence of information and big data in the future, the Bayesian method is expected to be used more actively in the water resources field.
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      The Bayesian method is a very useful statistical tool in various fields including water resources. Therefore, in this study, the background of the Bayesian statistics and its application to the water resources field are reviewed. First, the history of...

      The Bayesian method is a very useful statistical tool in various fields including water resources. Therefore, in this study, the background of the Bayesian statistics and its application to the water resources field are reviewed. First, the history of the Bayesian method from the birth to the present, and the achievements of Bayesian statisticians are summarized. Next, the derivation of the Bayes' theorem, which is the basis of the Bayesian method, is presented, and the roles of the three elements of the Bayes' theorem: priori distribution, likelihood function, and posteriori distribution are explained. In addition, the unique features and advantages of the Bayesian statistics are summarized. Finally, the cases in water resources where the Bayesian method is applied are summarized by dividing them into several categories. With a prevalence of information and big data in the future, the Bayesian method is expected to be used more actively in the water resources field.

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      참고문헌 (Reference)

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      10 Tajiki, M., "Recursive Bayesian estimation of conceptual rainfall‐runoff model errors in real‐time prediction of streamflow" Wiley 56 (56): WR025237-, 2020

      1 이재용, "베이지안 통계의 역사와 미래에 대한 조망" 한국통계학회 27 (27): 855-863, 2014

      2 Fienberg, S. E, "When did Bayesian inference become “Bayesian”?" ISBA 1 (1): 1-40, 2006

      3 Kim, Y. O., "Value of seasonal flow forecasts in Bayesian stochastic programming" ASCE 123 (123): 335-, 1997

      4 Raftery, A. E., "Using Bayesian model averaging to calibrate forecast ensembles" AMS 133 (133): 1155-1174, 2005

      5 Marshall, L., "Towards dynamic catchment modelling: A Bayesian hierarchical mixtures of experts framework" Wiley 21 (21): 847-861, 2007

      6 Kuczera, G., "Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters" Elsevier 331 (331): 161-177, 2006

      7 Tang, Y., "Tools for investigating the prior distribution in Bayesian hydrology" Elsevier 538 : 551-562, 2016

      8 McGrayne, S. B., "The theory that would not die: how Bayes’rule cracked the enigma code, hunted down Russian submarines, & emerged triumphant from two centuries of controversy" Yale University Press 2011

      9 Beven, K., "The future of distributed models:Model calibration and uncertainty prediction" Wiley 6 (6): 279-298, 1992

      10 Tajiki, M., "Recursive Bayesian estimation of conceptual rainfall‐runoff model errors in real‐time prediction of streamflow" Wiley 56 (56): WR025237-, 2020

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      17 Evin, G., "Pitfalls and improvements in the joint inference of heteroscedasticity and autocorrelation in hydrological model calibration" Wiley 49 (49): 4518-4524, 2013

      18 Duan, Q., "Multimodel ensemble hydrologic prediction using Bayesian model averaging" Elsevier 30 (30): 1371-1386, 2007

      19 Kuczera, G., "Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm" Elsevier 211 (211): 69-85, 1998

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      21 Liedloff, A. C., "Integrating indigenous ecological and scientific hydrogeological knowledge using a Bayesian network in the context of water resource development" Elsevier 499 : 177-187, 2013

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      27 Vrugt, J. A., "Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?" Springer 23 (23): 1011-1026, 2009

      28 Smith, M., "Dam risk analysis using Bayesian networks" 2006

      29 Thyer, M., "Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis" Wiley 45 (45): W00B14-, 2009

      30 Kuczera, G, "Comprehensive at-site flood frequency analysis using Monte Carlo Bayesian inference" Wiley 35 (35): 1551-1557, 1999

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      40 Perelman, L., "Bayesian networks for estimating contaminant source and propagation in a water distribution system using cluster structure" 426-435, 2010

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      62 Bates, B. C., "A markov chain monte carlo scheme for parameter estimation and inference in conceptual rainfall-runoff modeling" Wiley 37 (37): 937-947, 2001

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      71 Coles, S. G., "A Bayesian analysis of extreme rainfall data" Wiley 45 (45): 463-478, 1996

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