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Angelika van der Linde
Researcher at University of Bremen
Publications - 20
Citations - 13290
Angelika van der Linde is an academic researcher from University of Bremen. The author has contributed to research in topics: Covariance & Bayesian probability. The author has an hindex of 9, co-authored 20 publications receiving 11534 citations.
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Bayesian measures of model complexity and fit
TL;DR: In this paper, the authors consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined and derive a measure pD for the effective number in a model as the difference between the posterior mean of the deviances and the deviance at the posterior means of the parameters of interest, which is related to other information criteria and has an approximate decision theoretic justification.
Journal Article
Bayesian measures of model complexity and fit
TL;DR: The posterior mean deviance is suggested as a Bayesian measure of fit or adequacy, and the contributions of individual observations to the fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages.
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The deviance information criterion: 12 years on
TL;DR: In this paper, the essentials of our paper of 2002 are briefly summarized and compared with other criteria for model comparison, after some comments on the paper's reception and influence, we consider criticisms and proposals forimprovement made by us and others.
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DIC in variable selection
TL;DR: In this article, the posterior predictive entropy is related to the target yielding DIC and modifications thereof, and the adequacy of criteria for posterior predictive model comparison is also investigated depending on the comparison to be made.
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Variational Bayesian functional PCA
TL;DR: A Bayesian approach to analyze the modes of variation in a set of curves is suggested, based on a generative model thus allowing for noisy and sparse observations of curves.