CROSS-VALIDATION OF CONTROLLED DYNAMIC MODELS: BAYESIAN APPROACH

摘要

The best test of quality of an estimated model is its implementation in real application. However, the use of a bad model is typically too costly. Therefore, model validation is considered as an obligatory step in model learning, and extensive theory has been developed within statistical community. However, the available rules deal almost exclusively with independent data samples. Consequently, they are substantially disqualified for validation of dynamic models. This paper approaches the problem using Bayesian formulation and solution. An algorithm for validation of models estimated within practically important exponential family is presented. Performance of the algorithms is illustrated on simulated example.