Results 231 to 240 of about 828,317 (269)

Entropies of Likelihood Functions

1992
We show that the normalized likelihood function needed to get from a prior probability vector to the posterior that results from the minimum cross-entropy inference process has the highest entropy among all probability vectors satisfying an appropriate set of linear constraints. We regard the domains of the entropy and cross-entropy functions as groups.
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Information from the maximized likelihood function

Biometrika, 1985
Suppose 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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Likelihood-enhanced fast rotation functions

Acta Crystallographica Section D Biological Crystallography, 2004
Experiences with the molecular-replacement program Beast have shown that maximum-likelihood rotation targets are more sensitive to the correct orientation than traditional targets. However, this comes at a high computational cost: brute-force rotation searches can take hours or even days of computation time on current desktop computers.
Laurent C, Storoni, Airlie J, McCoy, Randy J, Read   +2 more
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An approximation to the modified profile likelihood function

Biometrika, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Comparison of Parametric and Empirical Likelihood Functions

Biometrika, 1989
A detailed study of differences between parametric and empirical likelihood surfaces is made. In particular first- and second-order expansions for log likelihood functions are developed in nonparametric and parametric situations, where attention is confined to inference on a smooth function of an r-variate mean.
DiCiccio, Thomas J., Hall, Peter, Romano, Joseph P.   +2 more
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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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