the variable data and expert knowledge such as a mathematically expected description of variable behavior and physically reasonable value ranges the calculation is repeated until the process converges to a predetermined convergence criterion for example the ratio of the current posterior maximum to the previous posterior maximum can be used as such a criterion once the calculation has converged the probability distribution for the parameters is established the data d used by the bayesian inference module 44 is variable data from the system being monitored it can be real time data or that obtained from the optional storage module 40 it can be a subset of the data being studied or training data while the probability distribution of the parameters does not enforce a choice of the parameters to be used in practice for a sufficiently peaked distribution a good approximation to the entire space can be used by choosing the set of values x of the parameters that yield the maximum value of p x d m the chosen parameter values x are then part of the concrete probabilistic model 48 the pdf of the concrete probabilistic model is p d x where the functional form of the pdf is given by m the abstract probabilistic model as an example if this method is applied to a particular instantiation of.