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A general framework for maximizing likelihood under incomplete data

Couso, Inès and Dubois, Didier A general framework for maximizing likelihood under incomplete data. (2018) International Journal of Approximate Reasoning, 93. 238-260. ISSN 0888-613X

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Official URL: https://doi.org/10.1016/j.ijar.2017.10.030


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, a major issue is to properly define the likelihood function to be maximized. This paper highlights the fact that there are several possible likelihood functions to be considered, depending on the purpose to be addressed, namely whether the behavior of the imperfect measurement process causing incompleteness should be included or not in the model, and what are the assumptions we can make or the knowledge we have about this measurement process. Various possible approaches, that differ by the choice of the likelihood function and/or the attitude of the analyst in front of imprecise information are comparatively discussed on examples, and some light is shed on the nature of the corresponding solutions.

Item Type:Article
Additional Information:https://www.sciencedirect.com/science/article/pii/S0888613X17306692
HAL Id:hal-02378365
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Universidad de Oviedo (SPAIN)
Laboratory name:
Deposited On:18 Nov 2019 13:01

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