ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Comparison of Methods for Summarizing and Simulating Non-Standard Distributions

5 September 2018, 11:10 – 11:30


Submitted by
Jody Muelaner
Jody Muelaner (The University of Bath), Kavya Jagan (The National Physical Laboratory)
When simulating uncertainties within non-linear models the output distributions are often non-Gaussian and not conforming to any standard distribution. Quantiles can be directly determined from the simulation results. However, a method of concisely describing the output distribution remains desirable so that, at a later date, quantiles for different probabilities may be determined or the complete sample may be regenerated as an input to some further simulation. A method which allows extrapolation to probability levels not feasible by numerical simulation would also be desirable. We compare a number of methods for summarizing non-standard distributions and for generating random numbers using these summary statistics. The methods compared include using: i) A Pearson distribution; ii) A Johnson distribution; iii) Distribution fitting based on Entropy; and iv) Fitting splines to quantiles. A number of non-linear models of measurements are used to simulate uncertainties with non-standard distributions and the accuracy of each method in recreating these distributions is then evaluated in terms of quantile determination.

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