Results 241 to 250 of about 390,909 (264)

Factoring the likelihood function

1996
Abstract The likelihood function provides an overall assessment of the relative merits of different members of a given family of statistical models, although this must be balanced against their relative complexity. However, as we saw in Section 3.6.3, we often require measures of precision of the estimates of individual parameters in the
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STATISTICAL INFERENCE WITH SIMULATED LIKELIHOOD FUNCTIONS

Econometric Theory, 1999
Summary: This paper considers classical test statistics, namely, the likelihood ratio, efficient score, and Wald statistics, for econometric models under simulation estimation. The simulated likelihood ratio, simulated efficient score, and simulated Wald test statistics are shown to be asymptotically equivalent.
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Learning to Rank with Likelihood Loss Functions

2016
According 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, Liang Yang 0003, Bo Xu 0009, Hongfei Lin, Kan Xu   +4 more
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Use of the likelihood function in inference.

Psychological Bulletin, 1965
D A, SPROTT, J G, KALBFLEISCH
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Evaluation of likelihood functions for Gaussian signals

IEEE Transactions on Information Theory, 1965
State 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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Fuzzy Probabilities Based on the Likelihood Function

2009
If 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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A survey on modern trainable activation functions

Neural Networks, 2021
Andrea Apicella, Francesco Donnarumma, F Isgrò   +2 more
exaly  

Self‐consistent molecular orbital methods 25. Supplementary functions for Gaussian basis sets

Journal of Chemical Physics, 1984
John A Pople, J Stephen Binkley, Frisch Michael J   +2 more
exaly  

Progress in the room-temperature optical functions of semiconductors

Materials Science and Engineering Reports, 2002
Aleksandra B Djurisic, E Herbert Li
exaly  

Probability and Likelihood Functions

2022
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