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4. Discussion and conclusions
Three main activities are currently developed under PAMINA in the area of SA: A review of SA
methods, a benchmark on SA methods and their application to analyse several PA models proposed
by different participants. The review of SA techniques consists in an exhaustive search of methods
of interest, screening and global methods, collecting information about its theoretical foundations,
and about the interpretation of ‘sensitivity’ behind them. Special attention is paid to the expected
benefits of using them and their shortcomings. PAMINA partners have shown more interest on
global methods than on screening methods. Among global methods, Monte Carlo based methods,
and more specifically regression based methods (linear and rank based) are among the most popular;
although not very powerful, they do not need specific sampling and may be used under the most
popular Monte Carlo techniques (SRS and LHS) used in a normal uncertainty analysis. Most powerful
techniques, such as Sobol indices or FAST, need specific samples and are quite expensive in
terms of number of runs needed. Most current research efforts are devoted to develop less expensive
computing algorithms.
Finally, the benchmark on SA methods is giving our group a good opportunity to test most interesting
methods and learning about them.
References�
[1] Bolado, R., Castaings, W. and Tarantola, S. (2008). Contribution to the Sample Mean Plot
for Graphical and Numerical Sensitivity Analysis. Submitted to Reliability Engineering and
System Safety.
[2] Box, G.E.P. and Draper, N.R. (1987). Empirical Model Building and Response Surfaces.
Wiley Series in probability and Mathematical Statistics. John Wiley & Sons, Inc.
[3] Conover, W.J. (1980). Practical Nonparametric Statistics. Second edition. Applied Probability
and Statistics. John Wiley and Sons, Inc.
[4] Cooke, R.M. and Van Noortwijk, J.M. (2000). Graphical Methods. In ‘Sensitivity analysis’,
Saltelli, A., Chan, K. and Scott, E.M. (Editors). Wiley Series in probability and statistics.
John Wiley & Sons Ltd.
[5] Cukier. R.I., Fortuin, C.M., Shuler, K.E., Petschek, A.G. and Schaibly, J.K. (1973). Study of
the Sensitivity of Coupled Reaction systems to Uncertainties in Rate Coefficients. I. Theory.
Journal of Chemical Physics, Vol. 59 (8), 3873-3878.
[6] Cukier. R.I., Schaibly, J.K. and Shuler, K.E. (1975). Study of the Sensitivity of Coupled Reaction
systems to Uncertainties in Rate Coefficients. III. Analysis of the Approximations.
Journal of Chemical Physics, Vol. 63 (3), 1140-1149.
[7] Helton J.C., Johnson, J.D., Sallaberry, C.J. and Storlie, C.B. (2006). Survey of Samplingbased
Methods for Uncertainty and Sensitivity Analysis. Reliability Engineering and System
Safety, Vol. 91, 1175-1209.
[8] Morris, M.D. (1991). Factorial Sampling Plans for Preliminary Computational Experiments.
Technometrics, Vol. 33, Number 2, 161-174.
[9] Saltelli, A., Tarantola, S. and Chan, K. (1999). A Quantitative, Model Independent Method
for Global Sensitivity Analysis of Model Output. Technometrics, Vol. 41, Number 1, 39-56.
[10] Saltelli, A. (2002). Making Best Use of Model Evaluations to compute Sensitivity Indices.
Computer Physics Communications, Vol. 145, 280-297.
[11] Schaibly, J.K. and Shuler, K.E. (1975). Study of the Sensitivity of Coupled Reaction systems
to Uncertainties in Rate Coefficients. II. Applications. Journal of Chemical Physics, Vol. 59
(8), 3879-3888.
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