FPGA Acceleration of the phylogenetic likelihood function for Bayesian MCMC inference methods [PDF]
BMC Bioinformatics, 2010 Background Likelihood (ML)-based phylogenetic inference has become a popular method for estimating the evolutionary relationships among species based on genomic sequence data.
Bakos Jason D, Zierke Stephanie
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Improving logistic regression on the imbalanced data by a novel penalized log-likelihood function. [PDF]
J Appl Stat, 2022 Logistic regression is estimated by maximizing the log-likelihood objective function formulated under the assumption of maximizing the overall accuracy. That does not apply to the imbalanced data.
Zhang L, Geisler T, Ray H, Xie Y.
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Likelihood decision functions [PDF]
Electronic Journal of Statistics, 2013 In both classical and Bayesian approaches, statistical inference is unified and generalized by the corresponding decision theory. This is not the case for the likelihood approach to statistical inference, in spite of the manifest success of the likelihood methods in statistics.
Marco Cattaneo
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Optimal distribution-free concentration for the log-likelihood function of Bernoulli variables [PDF]
Journal of Inequalities and Applications, 2023 This paper aims to establish distribution-free concentration inequalities for the log-likelihood function of Bernoulli variables, which means that the tail bounds are independent of the parameters.
Zhonggui Ren
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Maximum-likelihood methods in wavefront sensing: stochastic models and likelihood functions [PDF]
Journal of the Optical Society of America A, 2007 Maximum-likelihood (ML) estimation in wavefront sensing requires careful attention to all noise sources and all factors that influence the sensor data. We present detailed probability density functions for the output of the image detector in a wavefront sensor, conditional not only on wavefront parameters but also on various nuisance parameters ...
Harrison H. Barrett +2 more
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Using a Neural Network to Approximate the Negative Log Likelihood Function [PDF]
EPJ Web of ConferencesAn increasingly frequent challenge faced in HEP data analysis is to characterize the agreement between a prediction that depends on a dozen or more model parameters—such as predictions coming from an effective field theory (EFT) framework—and the ...
Liu Shenghua +6 more
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Using the Genetic Algorithm to Maximize the Likelihood Function of Normal Distribution [PDF]
المجلة العراقية للعلوم الاحصائية, 2008 In this research, the genetic algorithm (GA) has been carried out. This application is considered to be a manufacturing treatment to find the value which maximizes the likelihood function . An algorithm has been proposed to find the values which maximize.
همسة معن محمد ثابت
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THE SHAPE OF THE ONE-DIMENSIONAL PHYLOGENETIC LIKELIHOOD FUNCTION. [PDF]
Ann Appl Probab, 2017 Dinh V, Matsen FA.
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Bayesian speckle tracking. Part I: an implementable perturbation to the likelihood function for ultrasound displacement estimation. [PDF]
IEEE Trans Ultrason Ferroelectr Freq Control, 2013 Byram B, Trahey GE, Palmeri M.
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Let Master of Public Health Students Experience Statistical Reasoning [PDF]
Athens Journal of Health and Medical Sciences, 2020 Biostatistics is an integral part of any public health studentʼs curriculum. Most master-level public health students, lacking undergraduate training in calculus-centered mathematics, are often regarded as unprepared for conceptual knowledge, and hence ...
Qi Zheng
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