In this paper, the API SRA methodology bridges the gap between "chemical security reality" and "chemical security theory (that is, game theoretic models)", by providing quantitative inputs for game theoretic models and by reflecting on game theoretic results with respect to industrial practice.
Finally, we look at some of the implications and testable predictions of the model and conclude by reflecting on the prospects for determining which of the models, inside-out or outside-in, is more likely to be correct.
In the generic approach instead of exchanging problem-specific parameters between the analytical and simulation model, the interaction is governed by reflecting the impact of uncertainty obtained via simulation to the objective function of the analytical model.
When a neural network is trained on a compendium of data, it builds a predictive model based on those data, by reflecting a minimization in error when the network's prediction (its output) is compared with a known or expected outcome.
We therefore expand the basic security model but give it added value by reflecting the reality of human behavior within an airports organizational context.
This model allows us to study whether the changes that are predicted by the optimal-observer model are reflected by changes in latent variables in the decision process.
The non-linear feature of our model is reflected by three regimes, one of which is characterized by a unit root process and the random walk hypothesis while the lower and upper regimes are well captured by a stationary autoregressive process with mean reversion and predictability.
Under an initial assumption that treats all models as equally probable ('flat priors'), our posterior belief in one model over the other model is reflected by differences in the harmonic mean likelihoods generated by each model [50].
The change in the difference in the predicted probabilities of the outcome after introducing SAF to LVEF was estimated by the integrated discrimination index (IDI) (17), where a better model is reflected by a greater difference in the predicted probabilities.
These plots further reveal that the fluorescence readings generated by later cycles do not conform well to the LRE model, as reflected by the "spilling" of points off of the LRE line.
