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Learning to Rank with Likelihood Loss Functions
2016According to a given query in training set, the documents can be grouped based on their relevance judgments. If the group with higher relevance labels is in front of the one with lower relevance judgments, the ranking performance of ranking model could be perfect.
Yuan Lin 0001 +4 more
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Information from the maximized likelihood function
Biometrika, 1985Suppose x = (x1, ..., xj) is a random sample of either scalar or vector observations from a density f(x, c(), where w( E Q is partitioned into a set 0 = (01, ..., Or) of parameters of direct interest and 4 = (4 1, ..., 4,q) of nuisance parameters.
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Evaluation of likelihood functions for Gaussian signals
IEEE Transactions on Information Theory, 1965State variable techniques are used to derive new expressions for the likelihood function for Gaussian signals corrupted by additive Gaussian noise. The continuous time case is obtained as a limit of the discrete time case. The likelihood function is expressed in terms of the conditional expectation of the signal given only past and present observations,
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Use of the likelihood function in inference.
Psychological Bulletin, 1965D A, SPROTT, J G, KALBFLEISCH
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Fuzzy Probabilities Based on the Likelihood Function
2009If we interpret the statistical likelihood function as a measure of the relative plausibility of the probabilistic models considered, then we obtain a hierarchical description of uncertain knowledge, offering a unified approach to the combination of probabilistic and possibilistic uncertainty.
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Refinement of macromolecular structures by the maximum-likelihood method.
Acta Crystallographica Section D: Biological Crystallography, 1997G. Murshudov, A. Vagin, E. Dodson
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