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A Methodology for More Efficient Tail Area Sampling with Discrete Probability Distributions
Author(s) -
Park Sang Ryeol,
Kim Tae Woon,
Lee Byung Ho
Publication year - 1988
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.1988.tb00504.x
Subject(s) - monte carlo method , sampling (signal processing) , slice sampling , importance sampling , mathematics , statistics , rejection sampling , monte carlo integration , probability distribution , hybrid monte carlo , algorithm , computer science , statistical physics , markov chain monte carlo , physics , filter (signal processing) , computer vision
Monte Carlo Method is commonly used to observe the overall distribution and to determine the lower or upper bound value in statistical approach when direct analytical calculation is unavailable. However, this method would not be efficient if the tail area of a distribution is concerned. A new method, entitled Two‐Step Tail Area Sampling, is developed, which uses the assumption of discrete probability distribution and samples only the tail area without distorting the overall distribution. This method uses a two‐step sampling procedure. First, sampling at points separated by large intervals is done and second, sampling at points separated by small intervals is done with some check points determined at first‐step sampling. Comparison with Monte Carlo Method shows that the results obtained from the new method converge to analytic value faster than Monte Carlo Method if the numbers of calculation of both methods are the same. This new method is applied to DNBR (Departure from Nucleate Boiling Ratio) prediction problem in design of the pressurized light water nuclear reactor.