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Communication Dans Un Congrès Année : 2023

Maximum Likelihood Under Incomplete Information: Toward a Comparison of Criteria

Résumé

Maximum likelihood is a standard approach to computing a probability distribution that best fits a given dataset. However, when datasets are incomplete or contain imprecise data, depending on the purpose, a major issue is to properly define the likelihood function to be maximized. This paper compares several proposals in terms of their intuitive appeal, showing their anomalous behavior on examples.
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Dates et versions

hal-04109463 , version 1 (30-05-2023)

Identifiants

Citer

Inés Couso, Didier Dubois. Maximum Likelihood Under Incomplete Information: Toward a Comparison of Criteria. 8th International Conference on Soft Methods in Probability and Statistics (SMPS 2016), Sep 2016, Roma, Italie. pp.141-148, ⟨10.1007/978-3-319-42972-4_18⟩. ⟨hal-04109463⟩
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