Abstract
Estimation of probabilities by classical maximum likelihood estimators can give unreliable results when the number of cases is small. A Bayesian approach, where prior probabilities with Dirichlet distributions are used to temper the estimates, can reduce the variance enough to make the estimates useful. This is demonstrated by using this approach to estimate mortalities of severe infections from different sites, lungs, skin urinary tract, etc. The prior probabilities are provided in a hierarchical way, i.e. by deriving them from the same database, but without distinguishing between different sites of infection.
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Andreassen, S., Kristensen, B., Zalounina, A., Leibovici, L., Frank, U., Schønheyder, H.C. (2003). Hierarchical Dirichlét Learning – Filling in the Thin Spots in a Database. In: Dojat, M., Keravnou, E.T., Barahona, P. (eds) Artificial Intelligence in Medicine. AIME 2003. Lecture Notes in Computer Science(), vol 2780. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39907-0_38
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DOI: https://doi.org/10.1007/978-3-540-39907-0_38
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