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Model-based fault detection methods have the potential to reduce the size, weight, and cost of safety-critical aerospace systems. One obstacle to the application of these methods is a lack of appropriate tools to efficiently certify their reliability. This paper focuses on the false alarm analysis of a general fault detection scheme. The main difficulty of the false alarm analysis is the time-correlations...
In this paper, we show how temporal (i.e., time-series) Gaussian process regression models in machine learning can be reformulated as linear-Gaussian state space models, which can be solved exactly with classical Kalman filtering theory. The result is an efficient non-parametric learning algorithm, whose computational complexity grows linearly with respect to number of observations. We show how the...
In this paper we address the problem of border detection for textured images by exploiting the concept of Bayesian complexity minimization as a generalization of the MDL paradigm in the Bayesian context. We aim at determining if a small window at a certain location corresponds to a single or several texture classes, which here are modeled as Gaussian Markov Random Fields (GMRF), thereby detecting...
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