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Cosmetic changes only.

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Karthik 2021-07-16 15:51:00 +02:00
parent 4051549b27
commit 2d7bc36fee
1 changed files with 5 additions and 5 deletions
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10
+Simulator/@TwoDimensionalMOT/bootstrapErrorEstimation.m
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@ -9,20 +9,20 @@ function [LoadingRate, StandardError, ConfidenceInterval] = bootstrapErrorEstima
if ~isnan(CorrelationFactor) if ~isnan(CorrelationFactor)
SampleLength = floor(CorrelationFactor); SampleLength = floor(CorrelationFactor);
NumberOfBootsrapSamples = 1000; NumberOfBootsrapSamples = 1000;
MeanLoadingRatioInEachSample = zeros(1,NumberOfBootsrapSamples); MeanCaptureRatioInEachSample = zeros(1,NumberOfBootsrapSamples);
for SampleNumber = 1:NumberOfBootsrapSamples for SampleNumber = 1:NumberOfBootsrapSamples
BoostrapSample = datasample(NumberOfLoadedAtoms, SampleLength); % Sample with replacement BoostrapSample = datasample(NumberOfLoadedAtoms, SampleLength); % Sample with replacement
MeanLoadingRatioInEachSample(SampleNumber) = mean(BoostrapSample) / n; % Empirical bootstrap distribution of sample means MeanCaptureRatioInEachSample(SampleNumber) = mean(BoostrapSample) / n; % Empirical bootstrap distribution of sample means
end end
LoadingRate = mean(MeanLoadingRatioInEachSample) * ovenObj.ReducedFlux; LoadingRate = mean(MeanCaptureRatioInEachSample) * ovenObj.ReducedFlux;
Variance = 0; % Bootstrap Estimate of Variance Variance = 0; % Bootstrap Estimate of Variance
for SampleNumber = 1:NumberOfBootsrapSamples for SampleNumber = 1:NumberOfBootsrapSamples
Variance = Variance + (MeanLoadingRatioInEachSample(SampleNumber) - mean(MeanLoadingRatioInEachSample))^2; Variance = Variance + (MeanCaptureRatioInEachSample(SampleNumber) - mean(MeanCaptureRatioInEachSample))^2;
end end
StandardError = sqrt((1 / (NumberOfBootsrapSamples-1)) * Variance) * ovenObj.ReducedFlux; StandardError = sqrt((1 / (NumberOfBootsrapSamples-1)) * Variance) * ovenObj.ReducedFlux;
ts = tinv([0.025 0.975],NumberOfBootsrapSamples-1); % T-Score ts = tinv([0.025 0.975],NumberOfBootsrapSamples-1); % T-Score
ConfidenceInterval = LoadingRate + ts*StandardError; % 95% Confidence Intervals ConfidenceInterval = LoadingRate + ts*StandardError; % 95% Confidence Intervals