An adaptive sampling scheme guided by BART-with an application to predict processor performance

2010 ◽  
pp. n/a-n/a
Author(s):  
Qingzhao Yu ◽  
Bin Li ◽  
Zhide Fang ◽  
Lu Peng
Keyword(s):  
Adaptive Sampling ◽  
Sampling Scheme ◽  
Processor Performance

An Adaptive Sampling Scheme for Out-of-Core Simplification

2002 ◽  
Vol 21 (2) ◽  
pp. 111-118 ◽  
Author(s):  
Guangzheng Fei ◽  
Kangying Cai ◽  
Baining Guo ◽  
Enhua Wu
Keyword(s):  
Adaptive Sampling ◽  
Sampling Scheme

An Adaptive Sampling Scheme via Approximate Volume Sampling for Fingerprint-Based Indoor Localization

2019 ◽  
Vol 6 (2) ◽  
pp. 2338-2353 ◽  
Author(s):  
Haifeng Zheng ◽  
Min Gao ◽  
Zhizhang Chen ◽  
Xiao-Yang Liu ◽  
Xinxin Feng
Keyword(s):  
Adaptive Sampling ◽  
Indoor Localization ◽  
Sampling Scheme

A Generalized “Max-Min” Sample for Reliability Assessment With Dependent Variables

2014 ◽  
Author(s):  
Sylvain Lacaze ◽  
Samy Missoum
Keyword(s):  
Probability Density ◽  
Adaptive Sampling ◽  
Reliability Assessment ◽  
Joint Probability ◽  
Probability Of Failure ◽  
Sampling Scheme ◽  
Support Vector ◽  
Space Design ◽  
Novel Approach ◽  
Dependent Variables

This paper introduces a novel approach for reliability assessment with dependent variables. In this work, the boundary of the failure domain, for a computational problem with expensive function evaluations, is approximated using a Support Vector Machine and an adaptive sampling scheme. The approximation is sequentially refined using a new adaptive sampling scheme referred to as generalized “max-min”. This technique efficiently targets high probability density regions of the random space. This is achieved by modifying an adaptive sampling scheme originally tailored for deterministic spaces (Explicit Space Design Decomposition). In particular, the approach can handle any joint probability density function, even if the variables are dependent. In the latter case, the joint distribution might be obtained from copula. In addition, uncertainty on the probability of failure estimate are estimated using bootstrapping. A bootstrapped coefficient of variation of the probability of failure is used as an estimate of the true error to determine convergence. The proposed method is then applied to analytical examples and a beam bending reliability assessment using copulas.

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