Results 161 to 170 of about 23,661,782 (208)
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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
Abstract Bayesian 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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Abstract Bayesian 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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A Bayesian approach to sensitivity analysis
Health Economics, 1999Sensitivity analysis has traditionally been applied to decision models to quantify the stability of a preferred alternative to parametric variation. In the health literature, sensitivity measures have traditionally been based upon distance metrics, payoff variations, and probability measures.
Felli, James C., Hazen, Gordon B.
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Biometrika, 1978
A parametric model for partitioning individuals into mutually exclusive groups is given. A Bayesian analysis is applied and a loss structure imposed. A model-dependent definition of a similarity inatrix is proposed and estimates based on this matrix are justified in a decision-theoretic framework.
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A parametric model for partitioning individuals into mutually exclusive groups is given. A Bayesian analysis is applied and a loss structure imposed. A model-dependent definition of a similarity inatrix is proposed and estimates based on this matrix are justified in a decision-theoretic framework.
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Sensitivity analysis in Bayesian networks
1995For systems based on Bayesian networks, evidence is used to compute posterior probabilities for some hypotheses. Sensitivity analysis is concerned with questions on how sensitive the conclusion is to the evidence provided. After the basic definitions and an example we conclude that the heart of sensitivity analysis is to compute probabilities for the ...
Jensen, F. V. +2 more
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Abstract The book finishes with a chapter on Bayesian statistics. In a Bayesian analysis a prior distribution and a sample of data are combined to provide a refined posterior distribution. Hence, the posterior distribution can be seen as a fusion between the prior distribution and the observations.
Markus Neuhäuser, Graeme D. Ruxton
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Markus Neuhäuser, Graeme D. Ruxton
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2009
Abstract This article surveys modern Bayesian methods of estimating statistical models. It first provides an introduction to the Bayesian approach for statistical inference, contrasting it with more conventional approaches. It then explains the Monte Carlo principle and reviews commonly used Markov Chain Monte Carlo (MCMC) methods.
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Abstract This article surveys modern Bayesian methods of estimating statistical models. It first provides an introduction to the Bayesian approach for statistical inference, contrasting it with more conventional approaches. It then explains the Monte Carlo principle and reviews commonly used Markov Chain Monte Carlo (MCMC) methods.
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