M
Marina Vannucci
Researcher at Rice University
Publications - 185
Citations - 6324
Marina Vannucci is an academic researcher from Rice University. The author has contributed to research in topics: Feature selection & Prior probability. The author has an hindex of 40, co-authored 171 publications receiving 5381 citations. Previous affiliations of Marina Vannucci include Baylor College of Medicine & University of the Pacific (United States).
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Multivariate Bayesian variable selection and prediction
TL;DR: The marginal posterior distribution of the binary latent vector of the multivariate regression model with p regressors is derived and the approach illustrated on compositional analysis of data involving three sugars with 160 near infrared absorbances as regressors.
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Gene selection: a Bayesian variable selection approach
TL;DR: A hierarchical Bayesian model for gene (variable) selection is proposed and applied to cancer classification via cDNA microarrays where the genes BRCA1 and BRCa2 are associated with a hereditary disposition to breast cancer, and the method is used to identify a set of significant genes.
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Bayesian statistics and modelling
Rens van de Schoot,Sarah Depaoli,Ruth King,Ruth King,Bianca Kramer,Kaspar Märtens,Mahlet G. Tadesse,Marina Vannucci,Andrew Gelman,Duco Veen,Joukje Willemsen,Christopher Yau,Christopher Yau +12 more
TL;DR: This Primer on Bayesian statistics summarizes the most important aspects of determining prior distributions, likelihood functions and posterior distributions, in addition to discussing different applications of the method across disciplines.
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Bayesian Variable Selection in Clustering High-Dimensional Data
TL;DR: This article formulate the clustering problem in terms of a multivariate normal mixture model with an unknown number of components and use the reversible-jump Markov chain Monte Carlo technique to define a sampler that moves between different dimensional spaces.
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Bayesian wavelet regression on curves with application to a spectroscopic calibration problem
TL;DR: In this article, a Bayesian variable selection method using mixture priors was applied to the multivariate regression of predictands on wavelet coefficients for near-infrared (NIR) spectroscopy.