Initial Description of Multi-Modal Dynamic Models
Multiple models, neural networks, cluster analysis and probabilistic mixtures are prominent examples of situations when complex multi-modal models  are built using vast amount of data. Complexity and non-unicity of modified situation imply that resulting description depends heavily on the initial phase of search. The safest repetitive purely random search is mostly inhibited by computational complexity of the addressed task. For this reasons, various techniques have been designed. None of them, to our best knowledge, suits to cases when dynamic models are constructed. The paper describes a novel technique that fills this gap in a promising way. Essentially, the trial description is gradually split whenever there is possibility that a unimodal sub-model hides more modes.
KeywordsRandom Search Little Square Estimate Recursive Little Square Normal Mixture Probabilistic Mixture
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