Statistical network analysis computation times

The above discussion indicates that the reaction conditions have a significant influence on the SDE of guest molecules or ions. The SDAs can only play their SDE under appropriate gel conditions. Therefore, studies on the roles of guest molecules or ions by applying advanced computational techniques such as data mining, statistical analysis, and neutral networking to the improved synthetic database of the microporous compounds should include other factors such as reaction temperature, time, gel composition, and pH. 

Estimation of model error bars and sensitivity analyses are based rai the same principle. AU rate coefficients (or other model parameters) of a system are randomly varied within a certain range. The chemical evolution is then computed for each set of rate coefficients. For a network containing 4,000 reactions, the model is typically run 2,000 times with different sets of rate coefficients. The distribution of the rate coefficients can be either log-normal or log-uniform (see Fig. 4.5). The first choice implies that the mean value ko is a preferred value. This is usually the case for rate coefficients, which are measured with an uncertainty defined by statistical errors. The factor Fq, which defines the range of variation, can be a fixed factor for aU reactiOTis for a sensitivity analysis or specific to each reaction for an uncertainty propagation study. Use of the same Fq for all reactions, in the case of a sensitivity analysis, assures the modeller that an underestimated uncertainty factor will not bias the analysis. The results of thousands of runs are used differently to identify important reactions and to estimate model error bars. 

Artificial Neural Networks (ANNs) have been deemed successful in applications involving classification, identiflcation, pattern recognition, time series forecasting and optimisation. ANNs are distributed information-processing systems composed of many simple computational elements interacting across weighted connections. It was inspired by the architecture of the human brain. The ability of ANNs to model a complex stochastic system could be utilised in risk prediction and decision-making research, especially in areas where multi-variate statistical analysis is carried out. 

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