کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4920211 | 1429087 | 2017 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An evolutionary nested sampling algorithm for Bayesian model updating and model selection using modal measurement
ترجمه فارسی عنوان
الگوریتم نمونه گیری تست تکاملی برای به روز رسانی مدل بیزی و انتخاب مدل با استفاده از اندازه گیری مودال
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کلمات کلیدی
نمونه برداری نشت، استنتاج بیزی، به روز رسانی مدل، انتخاب مدل، الگوریتم تکاملی، اندازه گیری مودال،
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
مهندسی ژئوتکنیک و زمین شناسی مهندسی
چکیده انگلیسی
Nested sampling (NS) is a highly efficient and easily implemented sampling algorithm that has been successfully incorporated into Bayesian inference for model updating and model selection. The key step of this algorithm lies in proposing a new sample in each step that has a higher likelihood to replace the sample that has the lowest likelihood evaluated in the previous iteration. This process, also regarded as a constrained sampling step, has significant impact on the algorithm efficiency. This paper presents an evolutionary nested sampling (ENS) algorithm to promote the proposal of effective samples for Bayesian model updating and model selection by introducing evolutionary operators into standard NS. Instead of randomly drawing new samples from prior space, ENS algorithm proposes new samples from previously evaluated samples in light of their likelihood values without any evaluation of gradient. The main contribution of the presented algorithm is to greatly improve the sampling speed in the constrained sampling step by use of previous samples. The performances of the proposed ENS algorithm for model updating and model selection are examined through two numerical examples.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Structures - Volume 140, 1 June 2017, Pages 298-307
Journal: Engineering Structures - Volume 140, 1 June 2017, Pages 298-307
نویسندگان
Feng Qian, Wei Zheng,