statistical performance assessment of sequential Bayesian processors, based on information theory.

What is it about?

A novel method to characterise the efficacy and efficiency of different sequential Bayesian processor implementations is proposed. This method is based on concepts of probably approximately correct computation and information theory measures. The proposed approach is used to compare the performance of three different Bayesian estimation algorithms in the context of lithium-ion battery state-of-charge monitoring.

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Why is it important?

The novelty of the work lies in the construction of a comparative framework for sequential Bayesian processors, which allows probabilistic assessment of worst-case scenarios in terms of the performance exhibited by a given Bayesian estimation algorithm. Also, the proposed scheme includes the definition of two measures based on information theory, which allows to incorporate the inherent probabilistic nature of different implementations within the analysis, to conclude on its performance.