Abstract
The enormous generality of the Bayesian approach has been emphasised in the first lecture several times. It follows from the fact that the problem formulation and some of its properties are valid for the diverse set structure of the observations X, states K, and decisions D. It is surely desirable to master the whole richness of Bayesian tasks, and not to identify it with a special case. We already know that the class of Bayesian tasks is more than minimisation of the probability of a wrong decision.
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© 2002 Springer Science+Business Media Dordrecht
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Schlesinger, M.I., Hlaváč, V. (2002). Non-Bayesian statistical decision making. In: Ten Lectures on Statistical and Structural Pattern Recognition. Computational Imaging and Vision, vol 24. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-3217-8_2
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DOI: https://doi.org/10.1007/978-94-017-3217-8_2
Publisher Name: Springer, Dordrecht
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