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• W2018590165 abstract "We present a simple paradigm for fitting models, parametric and nonparametric, to noisy data, which resolves some of the problems associated with classical MSE algorithms. This is done by considering each point on the model as a possible source for each data point. The paradigm can be used to solve problems which are ill-posed in the classical MSE approach, such as fitting a segment (as opposed to a line). It is shown to be nonbiased and to achieve excellent results for general curves, even in the presence of strong discontinuities. Results are shown for a number of fitting problems, including lines, circles, elliptic arcs, segments, rectangles, and general curves, contaminated by Gaussian and uniform noise." @default.
• W2018590165 created "2016-06-24" @default.
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• W2018590165 date "2001-05-01" @default.
• W2018590165 modified "2023-10-02" @default.
• W2018590165 title "A Bayesian method for fitting parametric and nonparametric models to noisy data" @default.
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• W2018590165 doi "https://doi.org/10.1109/34.922710" @default.
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