Describing Uncertain Variables

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1 Describing Uncertain Variables L7

2 Uncertainty in Variables Uncertainty in concepts and models Uncertainty in variables Lack of precision Lack of knowledge Variability in space/time

3 Describing Uncertainty - Verbal Between x and y Less than x Approximately x Very unlikely / very likely More likely than not, Beyond reasonable doubt Good enough

4 Describing uncertainty - numerical Probability density function (PDF) Continuous or discontinuous Cumulative distribution function (CDF) CDF is (PDF)

5 f(x) PDF and CDF Normal distribution Cumulative normal distribution x

6 f(x) Example functions μ=1, σ= Normal Lognormal 0.6 Uniform Triangular 1 Triangular x

7 Nomenclature Variables in a model Parameters of a distribution Population all possible measurements Sample the measurements we have, a sub-set of the population (measurements may have been made on specimens/samples) Model a (simplified) description of reality Realisation a model calculated with a single set of values for the variables Ensemble a group of realisations Consequence, Probability, Risk

8 Assigning a Probability Density Function A PDF is a model of our data It is a simplification introduces some uncertainty by leaving out information Assign function Need to choose a distribution function Need to assign parameters to function Parameters derived from data and/or expert judgment

9 Types of Distributions Normal Log-normal Uniform Triangular Gamma Many variables far from zero follow this, e.g. porosity. Arithmetic mean follows this distribution Approximation to the distribution of positive variables close to zero. Geometric mean follows this distribution For example, the timing of a bounded event such as a leak Approximation for normal/lognormal distribution General purpose asymmetric distribution Exponential Poisson The time interval between random events, e.g. radioactive decays The number of random events in a specified time discrete function Binomial The number of successes from a trial, e.g. coin tossing discrete

10 Central tendency (Arithmetic) mean Geometric mean Harmonic mean Median Mode

11 Normal Distribution μ, σ Symmetrical about mean - to + Arithmetic mean = median = mode

12 f(x) Normal distribution Normal distribution Cumulative normal distribution x

13 Log-normal distribution μ, σ Asymmetrical 0 to + Arithmetic mean > median > mode Think carefully about transformation!

14 f(x) Lognormal distribution Lognormal Cumulative lognormal x

15 Useful formulae x y ln 2 exp ˆ y y s 1 exp ˆ ˆ y s 2 ˆ ln 2 s y y 1 ˆ ˆ ln s y

16 Uniform distribution a, b Symmetrical about mean a to b Arithmetic mean = median, mode not defined

17 f(x) Uniform distribution Uniform 0.6 Cumulative uniform x

18 Triangular Distribution a, b, c Symmetrical or asymmetrical a to c Arithmetic mean = median = mode, if symmetrical Arithmetic mean median mode, if asymmetrical

19 f(x) Triangular distributions Triangular 1 Triangular Cumulative triangular 1 Cumulative triangular x

20 Determining a PDF Sufficient data Enough data for statistical analysis, typically > 30 values Calculate PDF from data Limited data Too few for full analysis, <30 values Assume a type of PDF, estimate parameters from data Little or no data Elicitation Bayesian updating

21 Fitting PDFs to Data (1) Examine the data Spatial plots Time series plots Correlations Histograms and cumulative plots Quantile plots Statistical tests against distributions

22 More Frequency Histogram % % Frequency Cumulative % 80.00% 60.00% 40.00% % % Value

23 Experimental Quantile plot normal distribution Theoretical

24 Fitting PDFs to Data (2) Conceptual model of data Why do you believe that the distribution describes the data? Test the model against the data Outliers Multiple statistical populations

25 Fitting PDFs to Data (3) Criteria for assessing fit Limits Mean Standard deviation Minimum residual

26 Outliers Data transcription errors Measurement errors Unrepresentative samples Conceptual misunderstanding More complex model There must be a good reason to discard outliers!!

27 Complex Models Multiple statistical populations Contamination Mixtures Spatial variability Trend Geological variation Correlation Temporal variability Different analytical techniques

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