Vadim Strijov - Bayesian model selection and multimodelling

Multimodeling for learning-to-learn or meta-learning are discussed. The talk defines Bayesian strategies for local and universal model selection and multimodellings and discusses the principles of model selection.  Multimodels are used when a sample cannot be described by a single model. This happens when feature weights depend on the feature values. Though a multimodel is an interpretable generalization of a single model case, it can contain large number of similar models.  Pruning algorithms are constructed based on the suggested method for statistical model comparison.