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Likelihood Function

by J. Jeffrey Inman, Joonwook Park, Ashish Sinha
"... The log likelihood function is as follows: ..."
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The log likelihood function is as follows:

Likelihood function

by Stanislav Kolenikov
"... mixture by maximum likelihood: denormix package ..."
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mixture by maximum likelihood: denormix package

Likelihood Function

by Tim Gebbie, Diane Wilcox, Bongani Mbambiso, Empirical Results, Maximality Conditions
"... Wilcox, Gebbie & Mbambiso Spin, stochastic factor models, and a genetic algorithm A non-parametric clustering technique ..."
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Wilcox, Gebbie & Mbambiso Spin, stochastic factor models, and a genetic algorithm A non-parametric clustering technique

Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

by Jianqing Fan , Runze Li , 2001
"... Variable selection is fundamental to high-dimensional statistical modeling, including nonparametric regression. Many approaches in use are stepwise selection procedures, which can be computationally expensive and ignore stochastic errors in the variable selection process. In this article, penalized ..."
Abstract - Cited by 948 (62 self) - Add to MetaCart
penalized likelihood functions. The proposed ideas are widely applicable. They are readily applied to a variety of parametric models such as generalized linear models and robust regression models. They can also be applied easily to nonparametric modeling by using wavelets and splines. Rates of convergence

Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods

by John C. Platt - ADVANCES IN LARGE MARGIN CLASSIFIERS , 1999
"... The output of a classifier should be a calibrated posterior probability to enable post-processing. Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score. Howev ..."
Abstract - Cited by 1051 (0 self) - Add to MetaCart
The output of a classifier should be a calibrated posterior probability to enable post-processing. Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score

Non-Gaussian Likelihood Function

by Luca Amendola, Osservatorio Astronomico Di Roma A , 1995
"... We generalize the maximum likelihood method to non-Gaussian distribution functions by means of the multivariate Edgeworth expansion. We stress the potential interest of this technique in all those cosmological problems in which the determination of a non-Gaussian signature is relevant, e.g. in the a ..."
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We generalize the maximum likelihood method to non-Gaussian distribution functions by means of the multivariate Edgeworth expansion. We stress the potential interest of this technique in all those cosmological problems in which the determination of a non-Gaussian signature is relevant, e

Accelerating the Phylogenetic Likelihood Function

by Frederico Pratas, Pedro Trancoso, Alexandros Stamatakis, See Profile, See Profile, Ros Stamatakis
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.

Likelihood Functions for Stochastic Signals in

by White Noise, Tyrone E. Duncan , 1969
"... For a general stochastic signal in white noise absolute continuity is proved and the Radon-Nikodym derivative is given. These results were stated in a previous paper (Duncan 1968). Independent of the absolute continuity result, a modification is proved for the hypothesis with signal present. 1. ..."
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For a general stochastic signal in white noise absolute continuity is proved and the Radon-Nikodym derivative is given. These results were stated in a previous paper (Duncan 1968). Independent of the absolute continuity result, a modification is proved for the hypothesis with signal present. 1.

Likelihood Function Experiments

by Le Song, Arthur Gretton, Carlos Guestrin
"... Probabilistic graphical models •Represent complex dependencies between ..."
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Probabilistic graphical models •Represent complex dependencies between

APPROXIMATING THE LIKELIHOOD FUNCTION OF CMB EXPERIMENTS

by J. G. Bartlett, A. Blanchard, M. Douspis, M. Le Dour , 1998
"... ABSTRACT We discuss the problem of constraining cosmological parameters with cosmic microwave background band–power estimates. Because these latter are variances, they do not have gaussian distribution functions and, hence, the standard χ 2 –approach is not strictly applicable. A general purpose app ..."
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approximation to experimental band–power likelihood functions is proposed, which requires only limited experimental details. Comparison with the full likelihood function calculated for several experiments shows that the approximation works well. KEYWORDS: 1.
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