Abstract: Bayesian filters provide a statistical tool for dealing with measurement uncertainty. Bayesian filters estimate a state of dynamic system from noisy observations. These filters represent the ...

: The posterior; the probability of the hypothesis (e.g. that a parameter has a certain value) given the data: The likelihood of observing/generating the data given the hypothesis: The prior ...

Abstract: Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical ...

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ABSTRACT: This work introduces a novel Bayesian inspired regression method for the simultaneous estimation of model parameters and data uncertainties. The key mathematical result of this framework is ...

ABSTRACT: This paper investigates the application of machine learning techniques to optimize complex spray-drying operations in manufacturing environments. Using a mixed-methods approach that combines ...