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sql-derivative-sensitivity-analyser_demo [2019/06/01 15:47] alisa [Running guessing advantage analysis] |
sql-derivative-sensitivity-analyser_demo [2019/09/26 12:36] alisa [Running sensitivity analysis] |
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==== Running sensitivity analysis ==== | ==== Running sensitivity analysis ==== | ||
- | We are now ready to run the analysis. Click the blue button //Analyze//. Let us first set ε = 1 and β = 0.1. Click the green button //Run Analysis//. The most interesting value in the output that we see is the //relative error//. This can be interpreted as an upper bound on the relative distance of the noisy output from the actual output, which holds with probability 80%. There is unfortunately no strict upper bound on the additive noise, and it can potentially be infinite, though with negligible probability. Hence, we can only give a probabilistic upper bound on the noise, which is in our case hard-coded to 80%. | + | We are now ready to run the analysis. Click the blue button //Analyze//. Let us first set ε = 1 and β = 0.1, and set the slider "Confidence level of estimated noise" to 90%. Click the green button //Run Analysis//. The most interesting value in the output that we see is the //relative error//. This can be interpreted as an upper bound on the relative distance of the noisy output from the actual output, which holds with probability 90%. There is unfortunately no strict upper bound on the additive noise, and it can potentially be infinite, though with negligible probability. Hence, we can only give a probabilistic upper bound on the noise, which is in our case set to 90%. |
We can now play around with the model and see how the error can be reduced. | We can now play around with the model and see how the error can be reduced. |