Results 151 to 160 of about 23,661,782 (208)
Integrative Machine Learning and Bayesian Analysis Reveals Atrial Fibrillation as a Key Predictor of Severe COVID-19 Outcomes. [PDF]
Clin Transl SciKu YS +8 more
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Modeling Pubertal Growth Variability in Schoolchildren From Florianópolis, Santa Catarina, Brazil: A Bayesian Analysis. [PDF]
Am J Hum BiolGalvão LG +4 more
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Bayesian analysis and efficient algorithms for single-molecule fluorescence data and step counting. [PDF]
Biophys JMattamira C +5 more
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Biometrics, 1991
As a means of assessing the importance of variation in treatment effect among patient subsets, we derived posterior distributions for subset-specific treatment effects. The effects are represented by combinations of terms for treatment and treatment-by-covariate interaction effects in familiar regression models.
Dixon, Dennis O., Simon, Richard
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As a means of assessing the importance of variation in treatment effect among patient subsets, we derived posterior distributions for subset-specific treatment effects. The effects are represented by combinations of terms for treatment and treatment-by-covariate interaction effects in familiar regression models.
Dixon, Dennis O., Simon, Richard
openaire +3 more sources
Computing in Science & Engineering, 2001
One method of solving inverse problems in the presence of random variations (noise) emphasizes the importance of prior knowledge and uses Thomas Bayes's theorem from statistics. We describe a data analysis problem from experimental nuclear physics, requiring analysis of the energy spectrum from a nuclear reaction. We show how to use Bayesian methods to
Timothy C. Black, William J. Thompson
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One method of solving inverse problems in the presence of random variations (noise) emphasizes the importance of prior knowledge and uses Thomas Bayes's theorem from statistics. We describe a data analysis problem from experimental nuclear physics, requiring analysis of the energy spectrum from a nuclear reaction. We show how to use Bayesian methods to
Timothy C. Black, William J. Thompson
openaire +2 more sources
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008
Vector data are normally used for probabilistic graphical models with Bayesian inference. However, tensor data, i.e., multidimensional arrays, are actually natural representations of a large amount of real data, in data mining, computer vision, and many other applications.
Dacheng Tao +6 more
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Vector data are normally used for probabilistic graphical models with Bayesian inference. However, tensor data, i.e., multidimensional arrays, are actually natural representations of a large amount of real data, in data mining, computer vision, and many other applications.
Dacheng Tao +6 more
openaire +3 more sources
Electrophysiology Analysis, Bayesian
2014Bayesian analysis of electrophysiological data refers to the statistical processing of data obtained in electrophysiological experiments (i.e., recordings of action potentials or voltage measurements with electrodes or imaging devices) which utilize methods from Bayesian statistics.
Bassetto, G. +1 more
openaire +4 more sources