Introduction to Statistical Methods for Understanding Prediction Uncertainty in Simulation Models

Transcription

1 LA-UR Introduction to Statistical Methods for Understanding Prediction Uncertainty in Simulation Models Michael D. McKay formerly of the Statistical Sciences Group Los Alamos National Laboratory Presented at the 6 th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, Operations and Safety (NUTHOS-6), October 4-8, 2004, Nara, JAPAN

2 Lecture purpose: Introduce statistical theory and methods of analysis of prediction uncertainty in simulation models. I. Introduction II. Mathematical background Quantifying uncertainty Uncertainty importance and the correlation ratio Estimating the correlation ratio by R 2 III. Application to an environmental pathways model A computer experiment Features of model output Interpreting R 2 s Understanding R 2 s through data plots Cautions IV. Final thoughts and Acknowledgements

3 Prediction uncertainty for a discrete event simulation model Output is Cumulative Tons of Cargo Delivered Inputs are Use Rate, Fuel Flow, (8 in total). Tons of Cargo Day 15 high Prediction for Day 15 Best estimate calculation and design bounds. Given all the uncertainties, will the delivered cargo fall within the design bounds? low

4 Model predictions when inputs range over plausible values high Prediction for Day 15 low

5 It s about connecting-- Connecting physical and mathematical worlds Physical experiment System response w Physical conditions u Nature determines w = M(u). Computer experiment Model prediction y Inputs x Calculation determines y = m(x). System response is a complex quantity W for which y predicts features w = Γ(W). u may not be precisely known (or knowable). Conditions in addition to u might be responsible for w.

6 Prediction-uncertainty bands and important inputs All 8 inputs vary 1 input fixed at nominal 2 inputs fixed at 2 X 2 = 4 values Full predictionuncertainty band Reduced (conditional) uncertainty bands We want to find inputs that control spread.

7 Uncertainty due to model inputs D x x m y D y Plausible x values in generate region for y. Small perturbation of x generates perturbation of y. GLOBAL: Probability function over. D x Dx D x Dy f x ( x ) ( ) LOCAL: D y provides uncertainty weighting Probability function f y y induced by model m defines corresponding uncertainty over. D y

8 Why statistical methods? D x The space of possibilities that generate uncertainties is too big to be enumerated. Suppose uncertainties are due to plausible alternative values of p inputs defined on sets (intervals) characterized by I values (low, high, etc.) p inputs I values # points in input space inputs means there are 435 distinct pairs, 4060 triples, etc., as candidate important subsets of inputs.

9 Quantifying uncertainty Estimate where y is likely to be and characteristics of its probability distribution, for example: ˆ y 2 D, mean value, variance. µ y Tolerance bounds ( aˆ, b ˆ ) that have probability content p with confidence level 1 α 100%. Histogram or density function and empirical distribution function Fˆ t = Est. Pr y t. y σ y ( ) ( ) { }

10 Uncertainty importance (McKay 1997 Reliability Engineering and System Safety) Full model prediction using (all) x: y x = m x with x ~ f x ( ) ( ) ( ) s s Partition x = x U x where s {1,2, L, p} selects a subset (1 or more) of input variables. s Best restricted prediction using only x : ( s ) ( s ) s with ~ ( s = ) s y% x E y x x f x x = m x f x x dx ( ) ( ) s x s s s

11 Uncertainty importance (continued) s How does knowing x reduce uncertainty, or How close is y% to y (on average)? s Measuring uncertainty importance of x by how well it alone, compared to all x, predicts: E y% y y y% 2 ( ) = Var( ) Var( ) leads to the Pearson (1903) correlation ratio 2 η = Var( y% ) / Var( y) which is estimated (by R 2 ) from a sample of runs.

12 Linking formulas and data All 8 inputs vary 1 input fixed at nominal 2 inputs fixed at 2 X 2 = 4 values Var [ y] [ y % ] ( ) 2 Var h = Var y Var [ y% ]

13 Estimating the correlation ratio from an appropriate sample of runs s s s Let { x1, L, x n } be n values of x. s s s s For each x, let { x be r values of. i 1, L, x r } x Let { yij i = 1, L, n; j = 1, L, r} be the associated N = n r values from the model computer runs. Then, n r 1 y y estimates E y, r j = 1 = åå nr i = 1 j = 1 ij ( ) r 1 ( s s estimates E ) = ( s y ) i = å yi j y x = xi y % xi,

14 Estimating the correlation ratio (continued) n 2 åå( y ) ij - y nr [ y] SST = estimates Var, n i= 1 j= 1 r i= 1 j= 1 r 2 SSB = åå( ) estimates Var ( s y E ) i - y nr é y x ù ê ú. ë û y% Finally, Var é ( s ) 2 SSB E y x ù estimates 2 ê ú R = h = ë û. SST Var ( y) Of course, quality (validity) of estimates depends on a proper sampling plan / experimental design.

15 Experimental designs for estimating R 2 Replicated Latin hypercube sampling (rlhs) for subset size 1. Range of each input is divided into n equal probability intervals. Each interval is (conditionally) sampled once. Values are combined at random across input variables r times for N = n x r design points (r LHSs). Orthogonal array sampling (OAS) for subset size > 1. Like a rlhs but the values are combined in particular patterns, not at random. Usually, small n and larger r.

16 Application: model of environmental pathways 0. COMPARTMENTS 1. Vegetation surface 2. Vegetation interior 3. Terrestrial invertebrates 4. Small herbivores 5. Large herbivores 6. Insectivores 7. Predators 8. Litter 84 MODEL INPUTS Input Lower Upper Nominal X X

17 Experimental design for computer experiment p = 84 continuous model inputs discretized at 7 values or levels N = 343 of the possible 7 84 points in D x chosen using orthogonal array sampling (OAS) Note: In some examples that follow, there are 50 or 100 inputs and rlhs instead of OAS was used.

18 Output of pathways model for Compartment 3 when inputs range over plausible values Concentration Time in days

19 Candidates for analysis of pathways model output Scalar outputs: Equilibrium concentration (how high): Y 1 = C max Time to Equilibrium (how fast): Y 2 = t max = 0.9 C max Time dependent outputs: C(t) Normalized: C(t) / C max where C max = C(t max ) Standard time: C * (u) = C(u x t max ) / C max, 0 u 1

20 In the best of worlds: Pattern of ordered R 2 s for C max for 100 inputs from a sample of size 5000 R 2 Pattern suggests 4 groups of indistinguishable inputs Inputs ordered by R 2

21 What inputs do: Patterns of y% (average y) for top 9 inputs Average output level

22 In the not-so-best of worlds: Pattern of ordered R 2 s for C max for 100 inputs from a sample of size 10 R 2 Pattern suggests 1 group of indistinguishable inputs Inputs ordered by R 2

23 In a real world: R 2 s for the two scalar outputs with N = 343 R 2 s for t max R 2 s for C max Inputs ordered by R 2

24 Histogram of values of tmax

25 Values of t max plotted by values of inputs

26 In a real world: R 2 s for the two scalar outputs with N = 343 R 2 s for t max R 2 s for C max Inputs ordered by R 2

27 Histogram of values of Cmax

28 Values of C max plotted by value of inputs

29 Joint R 2 s for C max and Aliasing with OAS

30 R 2 s for Concentration as a function of time X24 R 2 X63 Time in days

31 Some main points Uncertainty quantification requires connecting physical and mathematical worlds. In the mathematical world, variation in inputs x is propagated by the simulation model to variation in outputs y. Variance of y is a measure of prediction uncertainty induced by inputs. Uncertainty importance of a subset of inputs refers to their contribution to prediction uncertainty.

32 Acknowledgements DECOMPOSITIONS H. H. Panjer (1973), On the Decomposition of Moments by Conditional Moments, The American Statistician, 27, CORRELATION RATIO R. L. Iman and S. C. Hora (1990), A Robust Measure of Uncertainty Importance for Use in Fault Tree System Analysis, Risk Analysis, 10, 3, B. Krzykacz (1990), SAMOS: A Computer Program for the Derivation of Empirical Sensitivity Measures of Results from Large Computer Models., GRS-A-1700, Gesellschaft fur Reaktorsicherheit (GRS) mbh, Garching, Republic of Germany. K. Pearson (1903), Mathematical Contributions to the Theory of Evolution, Proceedings of the Royal Society of London, 71, SAMPLING PLANS M. D. McKay, W. J. Conover and R. J. Beckman (1979), A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code, Technometrics, 21, 2, (Latin hypercube sampling) Art B. Owen (1992), Orthogonal Arrays for Computer Integration and Visualization, Statistica Sinica, 2, 2, GENERAL REFERENCES M. D. McKay (1995), Evaluating Prediction Uncertainty, NUREG/CR-6311, U.S. Nuclear Regulatory Commission and Los Alamos National Laboratory Report. M. D. McKay, J. D. Morrison, S. C. Upton (1999), Evaluating Prediction Uncertainty in Simulation Models, Computer Physics Communications, 117, A. Saltelli, K. Chan, E.M. Scott (Eds.), Sensitivity Analysis, John Wiley and Sons, Ltd. (2000).