A min-max regret approach to maximum likelihood inference under incomplete data
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Publication:2191235
DOI10.1016/j.ijar.2020.03.003zbMath1445.68217OpenAlexW3012258764MaRDI QIDQ2191235
Romain Guillaume, Dubois, Didier
Publication date: 24 June 2020
Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ijar.2020.03.003
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Cites Work
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- Set functions, games and capacities in decision making
- Ignorability and coarse data
- A general framework for maximizing likelihood under incomplete data
- Greedy randomized adaptive search procedures
- Probabilistic abduction without priors
- A definition of subjective possibility
- Possibilistic MDL: a new possibilistic likelihood based score function for imprecise data
- Learning from imprecise and fuzzy observations: data disambiguation through generalized loss minimization
- On Fuzzy-Mathematical Programming
- Likelihood and Bayesian Inference from Selectively Reported Data
- Upper and Lower Probabilities Induced by a Multivalued Mapping
- Decision-Making in a Fuzzy Environment
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