SPOP : A code for the analysis of system model response uncertainty and sensitivity analysis for model output : Program description and user guideFunded under: JRC-RADWASTE 5C
The SPOP (Statistical Post Processor) code is a tool to perform uncertainty and sensitivity analyses on the output of a user implemented model. It has been developed at the Joint Research Centre of Ispra as part of the LISA package. SPOP performs Sensitivity Analysis (SA) and Uncertainty Analysis (UA) on a sample output from a Monte Carlo simulation. The sample is generated by the user and contains values of the output variable (in the form of a time series) and values of the input variables for a set of different simulations (runs), which are realised by varying the model input parameters. The user may generate the Monte Carlo sample with the PREP preprocessor, another module of the LISA package. The SPOP code is completely written in FORTRAN 77 using structured programming. Among the tasks performed by the code are the computation of Tchebycheff and Kolmogorov confidence bounds on the output variable (UA), and the use of effective non-parametric statistics to rank the influence of model input parameters (SA). The statistics employed are described in the present manual.
Bibliographic Reference: EUR 12700 EN (1990) 41 pp., MF, ECU 4, blow-up copy ECU 6.25
Record Number: 199011055 / Last updated on: 1994-12-01
Original language: en
Available languages: en