Results 211 to 220 of about 309,134 (265)
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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
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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
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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
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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
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1998
In many fields of research the following problem is encountered: a large collection of data is given for which a detailed theory is yet missing. To gain insight into the underlying problem it is important to reveal the interrelationships in the data and to determine the relevant input and response quantities.
von der Linden, W. +2 more
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In many fields of research the following problem is encountered: a large collection of data is given for which a detailed theory is yet missing. To gain insight into the underlying problem it is important to reveal the interrelationships in the data and to determine the relevant input and response quantities.
von der Linden, W. +2 more
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The Practice of Bayesian Analysis
Technometrics, 1999The purpose of this book is twofold. First, it has been written to advertise the advantages a Bayesian analysis can bring. New statistical and decision models can be tailored to the unique beliefs, values and needs of the user, and the implications of the data she collects can be analysed with reference to this underlying structure.
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WIREs Cognitive Science, 2010
AbstractBayesian methods have garnered huge interest in cognitive science as an approach to models of cognition and perception. On the other hand, Bayesian methods for data analysis have not yet made much headway in cognitive science against the institutionalized inertia of 20th century null hypothesis significance testing (NHST).
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AbstractBayesian methods have garnered huge interest in cognitive science as an approach to models of cognition and perception. On the other hand, Bayesian methods for data analysis have not yet made much headway in cognitive science against the institutionalized inertia of 20th century null hypothesis significance testing (NHST).
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