The problem “data → the model, explaining the data” should be considered as the basic one for any area of science.
For the construction of the logic and probabilistic (LP) risk model of the LP-modeling class we describe the following methods: complete disjunctive normal form, the shortest paths of successful performance, minimum cuts of failures, associative LP-risk models, tabular setting of LP-models, LP-risk models for several aims, scenario, complex and dynamic LP-risk models.
We also describe the procedure of building the LP-model of this class: development of the risk scenario, writing down the L-risk model according to the scenario, transition from the L-risk model to the P-risk model by orthogonalization.
The LP-models of the LP-modeling class is used as the basis for building risk model of classes LP-classification, LP-efficiency and LP-forecasting. Therefore the description of the issues concerning building the model of LP-modeling class is of greatest importance. The development of scenario can be make by experts well knowing and understanding the functioning of the system.
LP-risk models are transparent, very good for risk analysis of systems and for management purposes. Neither scoring techniques, nor neural networks does not comply with these requirements and the Kalman’s rule.