Calibration, uncertainty, smt else (Paul) - Derived uncertainty
10 important questions on Calibration, uncertainty, smt else (Paul) - Derived uncertainty
THere's a relation between T and Qdrain, but we do not know its exact form
When is the (un)certainty covariance plot an elipse?
When the covplot is nearly a circle, there's hardly any correlation
What is the order of the fitting parameter, deriving quantity and getting their uncertainties procedure?
2. Plot the model
3. Add observations
4. Plot model + observations
5. Fit parameters, eg 2
6. Plot model + fitted parameters > gives better representation based on observations
7. Get stats on fitted parameters (available in R), which gives uncertainty measurse like sd/var/mean which you can plot into a boxplot
8. Get/choose derived quantities, using the fitted parameters
9. Calculate slope of quantity wrt parameter
10. Get general stats and plot them into boxplot and covplot
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Why is slope calculation necessary within this process?
What happens to uncertainty of derived quantity when plotting with more fitted parameters?
What is the Gaussian escape?
2. We assume f is linear, thus f(x) = ax+b
3. Thus: if E[x] and VAR[X] known, if f is linear, if Y is gaussian, then our problem is solved
Explain the Gaussian escape for derived uncertainty example
- mean(T) and var(T) are known from fitting procedure
- F is linear - assume approximately true
- Y is Gaussian - assume approx true
Problem is approximately solved
Why not use a Latin Hypercube Sampling?
- Works only for independent variables
- Parameters are almost never independent after fitting
What is the covar of a linear function?
How is fitting done? Write down the formula
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