Results 271 to 280 of about 24,269,673 (319)
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Technometrics, 2004
trol charts, and their relationship to process capability. Chapter 7, “Preparing for Control Charts,” presents procedures for determining sample size and frequency, with some comparison of variable and attribute control charts.
A. Gelman +5 more
semanticscholar +1 more source
trol charts, and their relationship to process capability. Chapter 7, “Preparing for Control Charts,” presents procedures for determining sample size and frequency, with some comparison of variable and attribute control charts.
A. Gelman +5 more
semanticscholar +1 more source
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
openaire +2 more sources
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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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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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.
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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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Statistical Decision Theory and Bayesian Analysis, Second Edition
, 1993J. Berger
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Bayesian analysis of neuroimaging data in FSL
NeuroImage, 2009M. Woolrich +8 more
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