Priors used in the analysis
Location Reparameterization and Default Priors for Statistical Analysis
... Normality is widely used as an approximation for sums of inde- pendent variables. Recent theory establishes that sums of log densi- ties provides an approximate exponential model, for which accurate p-values are ...
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Nonparametric Factor Analysis with Beta Process Priors
... factor analysis (BP- FA) model for performing nonparametric factor anal- ysis with a potentially infinite number of ...prior used for mixture mod- eling, the beta process is a fully Bayesian prior that as- ...
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Priors from DSGE Models for Dynamic Factor Analysis
... be used to simulate a new set of ...method used in this paper was developed by DelNegro and Schorheide ...be used to implement the so-called Minnesota ...
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Bayesian fmri time series analysis with spatial priors
... We used a design matrix comprising two regressors, the first being a boxcar with a period of 20 scans and the second a ...We used spatial precision parameters a 1 = a 2 = 1 and generated two N = 1024 ...
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Bayesian analysis of ARMA models using noninformative priors
... els used in econometrics, for example the Simultaneous Equations ...Bayesian analysis of the posteriors of locally nonidenti¯ed parameters is still quite small though, see ...di®use priors are ...
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Global tractography with embedded anatomical priors for quantitative connectivity analysis.
... However, due to the simplicity of the FiberCup dataset much less control points could have been used. Future improvements of this method would be to adjust the number of control points of the spline depending on ...
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Deep unsupervised state representation learning with robotic priors: a robustness analysis
... robotic priors: a robustness analysis Timoth´ee Lesort 1 , Mathieu Seurin 1,2 , Xinrui Li, Natalia D´ıaz-Rodr´ıguez and David Filliat Abstract— Our understanding of the world depends highly on our capacity ...
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Bayesian multifractal analysis of multi-temporal images using smooth priors
... Texture analysis can be conducted within the mathematical frame- work of multifractal analysis (MFA) via the study of the regularity fluctuations of image ...Successfully used in various ap- ...
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Updating preferences with multiple priors
... the analysis is formulating a definition of dynamic consistency that sticks as narrowly as possible to the notion, mentioned above, that ex ante optimal contingent choices should be respected when a planned-for ...
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Statistical Analysis of Location Parameter of Inverse Gaussian Distribution Under Noninformative Priors
... Noninformative priors (Uniform and Jeffreys) are assumed to be the prior distributions for the location parameter as the shape parameter of the distribution is considered to be ...are used for ...
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BAYESIAN ANALYSIS FOR THE PAIRED COMPARISON MODEL WITH ORDER EFFECTS (USING NON-INFORMATIVE PRIORS)
... Abstract Sometimes it may be difficult for a panelist to rank or compare more than two objects or treatments at the same time. For this reason, paired comparison method is used. In this study, the Davidson and ...
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The Value of Consensus Priors: A Response to Simpson
... consensus priors: A response to Simpson In our article ‘Rigorous Large-Scale Educational RCTs Are Uninformative: Should We Be Concerned?’ we demonstrated that a surprisingly large number of educational trials are ...
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Nonparametric priors for vectors of survival functions
... survival analysis, our results connect to a very active area of research in Bayesian nonparametric ...commonly used in Bayesian inference are not well suited for dealing with regression problems and a lot ...
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Diffusion-based spatial priors for imaging
... density priors on GLM ...spatial priors using graph Laplacians and diffusion ...is used to update hyperparameters of covariance components, which embody empirical spatial ...subjects) analysis ...
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Reconciling "priors" and "priors" without prejudice?
... Abstract There are two major routes to address linear inverse problems. Whereas regularization-based approaches build estimators as solutions of penalized regres- sion optimization problems, Bayesian estimators rely on ...
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Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration
... cointegration analysis in vector autoregressive VAR] models, for instance the computation of posterior ...cointegration analysis, including prior speci cation, simulation schemes for posterior ...
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Reconciling "priors" & "priors" without prejudice? (research report)
... Reconciling “priors” & “priors” without prejudice? Résumé : Il existe deux grandes familles de méthodes pour résoudre les problèmes linéaires inverses. Tandis que les approches faisant appel à la ...
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Description and the Problem of Priors
... If clear weather has typically followed cool water and fog has typically followed warm water, then the sender evolves to nearly always send one type of signal when- ever she observes cool water and the other whenever she ...
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Hierarchical Priors for Bias Parameters in Bayesian Sensitivity Analysis for Unmeasured Confounding
... Hierarchical priors borrow information from C in order to learn about bias from U ...sensitivity analysis which assumes zero correlation between measured and unmeasured ...mass priors that force γ CU ...
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Regression modelling using priors depending on Fisher information covariance kernels (I priors)
... The classical ANOVA decomposition The standard one-way ANOVA is essentially a linear regression model which allows comparison of means from two or more samples. Given sets of observations y j = { y 1j , . . . , y n j j } ...
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