Search results for "Prior probability"

showing 7 items of 47 documents

Discussion of "Objective Priors: An Introduction for Frequentists" by M. Ghosh

2011

Discussion of "Objective Priors: An Introduction for Frequentists" by M. Ghosh [arXiv:1108.2120]

Statistics and ProbabilityMethodology (stat.ME)FOS: Computer and information sciencesGeneral MathematicsPhilosophyPrior probabilityStatistics Probability and UncertaintyMathematical economicsStatistics - Methodology
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Unacceptable implications of the left haar measure in a standard normal theory inference problem

1978

For a very common statistical problem, inference about the mean of a normal random variable, some inadmissible consequences of the left Haar invariant prior measure, which is that recommended as a suitable prior by Jeffreys’ multivariate rule and by the methods of Villegas and Kashyap, are uncovered and investigated.

Statistics and ProbabilityNormal distributionStatisticsPrior probabilityInferenceHaarStatistics Probability and UncertaintyInvariant (mathematics)Standard normal tableMeasure (mathematics)MathematicsHaar measureTrabajos de Estadistica Y de Investigacion Operativa
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Bayesian measures of surprise for outlier detection

2003

From a Bayesian point of view, testing whether an observation is an outlier is usually reduced to a testing problem concerning a parameter of a contaminating distribution. This requires elicitation of both (i) the contaminating distribution that generates the outlier and (ii) prior distributions on its parameters. However, very little information is typically available about how the possible outlier could have been generated. Thus easy, preliminary checks in which these assessments can often be avoided may prove useful. Several such measures of surprise are derived for outlier detection in normal models. Results are applied to several examples. Default Bayes factors, where the contaminating…

Statistics and Probabilitybusiness.industryApplied MathematicsBayesian probabilityPosterior probabilityPattern recognitionBayes factorStatisticsPrior probabilityOutlierNuisance parameterAnomaly detectionArtificial intelligenceStatistics Probability and UncertaintybusinessMathematicsStatistical hypothesis testingJournal of Statistical Planning and Inference
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A New Technique for Vibration-Based Diagnostics of Fatigued Structures Based on Damage Pattern Recognition via Minimization of Misclassification Prob…

2017

Vibration-based diagnostics provide various methods to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration response. Research in vibration-based damage recognition has been rapidly expanding over the last few years. The basic idea behind this technology is that modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties will cause detectable changes in the modal properties. In investigations, many techniques were applied to recognize damage in structural and mechanical systems, b…

VibrationMechanical systemEngineeringNaive Bayes classifierModalbusiness.industryPrior probabilityPattern recognition (psychology)Pattern recognitionArtificial intelligenceMinificationLinear discriminant analysisbusiness
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Multiple Mean Models of Statistical Shape and Probability Priors for Automatic Prostate Segmentation

2011

International audience; Low contrast of the prostate gland, heterogeneous intensity distribution inside the prostate region, imaging artifacts like shadow regions, speckle and significant variations in prostate shape, size and in- ter dataset contrast in Trans Rectal Ultrasound (TRUS) images challenge computer aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose a probabilistic framework for automatic initialization and propagation of multiple mean parametric models derived from principal component analysis of shape and posterior probability information of the prostate region to segment the prostate. Unlike traditional statistical models of shape and int…

[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industryPosterior probability[INFO.INFO-IM] Computer Science [cs]/Medical ImagingProbabilistic logicInitializationStatistical modelPattern recognition02 engineering and technology030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicinePrior probabilityParametric modelPrincipal component analysis[INFO.INFO-IM]Computer Science [cs]/Medical Imaging0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingSegmentationArtificial intelligencebusinessMathematics
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Evaluation of the areal material distribution of paper from its optical transmission image

2011

International audience; The goal of this study was to evaluate the areal mass distribution (defined as the X-ray transmission image) of paper from its optical transmission image. A Bayesian inversion framework was used in the related deconvolution process so as to combine indirect optical information with a priori knowledge about the type of paper imaged. The a priori knowledge was expressed in the form of an empirical Besov space prior distribution constructed in a computationally effective way using the wavelet transform. The estimation process took the form of a large-scale optimization problem, which was in turn solved using the gradient descent method of Barzilai and Borwein. It was de…

[PHYS]Physics [physics]ta114Computer scienceGaussianWavelet transform010103 numerical & computational mathematicsCondensed Matter Physics01 natural sciences030218 nuclear medicine & medical imagingElectronic Optical and Magnetic MaterialsTikhonov regularization03 medical and health sciencessymbols.namesake0302 clinical medicinePrior probabilityPhysical SciencessymbolsBesov spaceA priori and a posterioriDeconvolution0101 mathematicsGradient descentInstrumentationAlgorithm
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Unawareness, Priors and Posteriors

2008

Abstract. This note contains first thoughts on awareness of unawareness in a simple dynamic context where a decision situation is repeated over time. The main consequence of increasing awareness is that the model the decision maker uses, and the prior which it contains, becomes richer over time. The decision maker is prepared to this change, and we show that if a projection-consistency axiom is satisfied unawareness does not affect the value of her estimate of a payoff-relevant conditional probability (although it may weaken confidence in such estimate). Probability-zero events however pose a challenge to this axiom, and if that fails, even estimate values will be different if the decision …

media_common.quotation_subjectConditional probabilityContext (language use)CertaintyVariable (computer science)Prior probabilityStatisticsEconometricsAwareness of Unawareness Model UncertaintyGeneral Economics Econometrics and FinanceValue (mathematics)FinanceAxiomMathematicsSimple (philosophy)media_common
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