The focal point of this paper is to provide a rapproachement between these two paradigms and propose a novel probabilistic framework for system identification. The main idea in this line of research is to "discard" sets of measure at most epsilon, where epsilon is a probabilistic accuracy, from the set of deterministic estimates. Therefore, we are decreasing the so-called worst-case radius of information at the expense of a given probabilistic "risk."

A Probabilistic Approach to Optimal Estimation Part I: Problem Formulation and Methodology

Dabbene Fabrizio;Tempo Roberto
2012

Abstract

The focal point of this paper is to provide a rapproachement between these two paradigms and propose a novel probabilistic framework for system identification. The main idea in this line of research is to "discard" sets of measure at most epsilon, where epsilon is a probabilistic accuracy, from the set of deterministic estimates. Therefore, we are decreasing the so-called worst-case radius of information at the expense of a given probabilistic "risk."
2012
System identification
optimal algorithms
uncertain systems
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/226451
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