Self-adaptive lower confidence bound: A new general and effective prescreening method for Gaussian Process surrogate model assisted evolutionary algorithms

2012 ◽  
Author(s):  
Bo Liu ◽  
Qingfu Zhang ◽  
Francisco V. Fernandez ◽  
Georges Gielen
Keyword(s):  
Evolutionary Algorithms ◽  
Gaussian Process ◽  
Surrogate Model ◽  
Confidence Bound ◽  
Lower Confidence ◽  
Lower Confidence Bound ◽  
Self Adaptive

scholarly journals Improved Gaussian Process Acquisition for Targeted Bayesian Optimization

2021 ◽  
pp. 12-18
Author(s):  
Peter Mitic ◽  
Keyword(s):  
Gaussian Process ◽  
Optimization Problem ◽  
Bayesian Optimization ◽  
Stochastic Algorithm ◽  
Confidence Bound ◽  
Noise Component ◽  
Simple Modification ◽  
Lower Confidence ◽  
Lower Confidence Bound ◽  
Long Time

A black-box optimization problem is considered, in which the function to be optimized can only be expressed in terms of a complicated stochastic algorithm that takes a long time to evaluate. The value returned is required to be sufficiently near to a target value, and uses data that has a significant noise component. Bayesian Optimization with an underlying Gaussian Process is used as an optimization solution, and its effectiveness is measured in terms of the number of function evaluations required to attain the target. To improve results, a simple modification of the Gaussian Process ‘Lower Confidence Bound’ (LCB) acquisition function is proposed. The expression used for the confidence bound is squared in order to better comply with the target requirement. With this modification, much improved results compared to random selection methods and to other commonly used acquisition functions are obtained.

scholarly journals Analysis of the specificity of the SD Biosensor Standard Q Ag-Test based on Slovak mass testing data

2020 ◽  
Author(s):  
Michal Hledík ◽  
Jitka Polechová ◽  
Mathias Beiglböck ◽  
Anna Nele Herdina ◽  
Robert Strassl ◽  
...  
Keyword(s):  
False Positives ◽  
Confidence Bound ◽  
Worst Case ◽  
Lower Confidence ◽  
Lower Confidence Bound ◽  
Testing Data ◽  
Worst Case Approach

AbstractFrom 31.10. - 1.11.2020 Slovakia has used the SD Biosensor Standard Q Ag-Test for nationwide tests for SARS-CoV-2, in which 3,625,332 persons from 79 counties were tested. Based on this data, we calculate that the specificity of the test is at least 99.6% (with a 97.5% one-sided lower confidence bound). Our analysis is based on a worst case approach in which all positives are assumed to be false positives. Therefore, the actual specificity is expected to exceed 99.6%.

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