کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6447646 1641782 2016 46 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Application of the transitional Markov chain Monte Carlo algorithm to probabilistic site characterization
ترجمه فارسی عنوان
استفاده از الگوریتم مونت کارلو زنجیره مارکوف انتقالی به خصوصیات احتمالی سایت
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات مهندسی ژئوتکنیک و زمین شناسی مهندسی
چکیده انگلیسی
This paper applies the transitional Markov chain Monte Carlo (TMCMC) algorithm to probabilistic site characterization problems. The purpose is to characterize the statistical uncertainties in the spatial variability parameters based on the cone penetration test (CPT) dataset. The spatial variability parameters of interest include the trend function, standard deviation and scale of fluctuation for the spatial variability, and so on. In contrast to the Metropolis-Hastings (MH) algorithm, the TMCMC algorithm is a tune-free algorithm: it does not require the specification of the proposal probability density function (PDF), hence there is no need to tune the proposal PDF. Also, there is no burn-in period to worry about, and the convergence issue is mild for TMCMC because the samples spread widely. Moreover, it can estimate the model evidence, a quantity essential for Bayesian model comparison, without extra computation cost. The effectiveness for the TMCMC algorithm is demonstrated through simulated examples and a real case study.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Geology - Volume 203, 25 March 2016, Pages 151-167
نویسندگان
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