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Model Consistency

Colección

We dedicate this second block to the model consistency problem. We start with the problem of physical consistency by indicating that not all formulas are valid to represent a physical variable when written in terms of other basic variables. We formulate a consistency condition in terms of a functional equation and show the only valid form. Second, we discuss probability consistency and show that some proposed models are contradictory from the point of vie of probability theory. We define operation stable consistency and suggest the use of reproductive models when possible. Finally, we deal with extreme value consistency, providing some rules for dsatisfying this condition and indicating some common errors found in the literature when using extreme value models in engineering practice.

Autores Enrique Castillo
Fecha 17/09/2019 Idioma Ingles

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Contenido

Physical model consistency. Model building

We deal with the problem of physical consistency of formulas. First, we derive the general formula for valid physical formulas and show how the commonly used formulas are of this form. Next, we discuss the compatibility of variable ranges, that can be violated if we use some common models.

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Probability consistent models. Model building

We deal with the problem we can face when building pprobability models to represent the reality being observed. We discuss what conditions lead to under, over and exact model definitions. In particular, we warn the reader about specifying inconsistent conditions, because no model can satisfy them. Finally, we recommend Bayesian networks, as the better way to define models and provide some illustrative examples.

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Extreme value consistent models. Model building

It is explained how the extreme distribution associated with a given family of distributions reduces to obtaining the limit of a simple expression, which is given. A table with the limit distributions of the most well known distributions is given. Finally, Gumbel and reverse Gumbel probability papers are used to identify the extreme models from a sample.

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