J
Juan Antonio Cano
Researcher at University of Murcia
Publications - 20
Citations - 727
Juan Antonio Cano is an academic researcher from University of Murcia. The author has contributed to research in topics: Prior probability & Bayes factor. The author has an hindex of 6, co-authored 20 publications receiving 673 citations.
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Journal ArticleDOI
An overview of robust Bayesian analysis
James O. Berger,Elías Moreno,Luis R. Pericchi,M. Jesús Bayarri,José M. Bernardo,Juan Antonio Cano,Julián de la Horra,Jacinto Martín,David Rios-Insua,Bruno Betrò,Anirban DasGupta,Paul Gustafson,Larry Wasserman,Joseph B. Kadane,Cid Srinivasan,Michael Lavine,Anthony O'Hagan,Wolfgang Polasek,Christian P. Robert,Constantinos Goutis,Fabrizio Ruggeri,G. Salinetti,Siva Sivaganesan +22 more
TL;DR: An overview of the subject of robust Bayesian analysis is provided, one that is accessible to statisticians outside the field, and recent developments in the area are reviewed.
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Robust Bayesian Analysis with ϵ‐Contaminations Partially Known
Elías Moreno,Juan Antonio Cano +1 more
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A probabilistic model for analysing the effect of performance levels on visual behaviour patterns of young sailors in simulated navigation
TL;DR: Results show that top ranking sailors perform a low recurrence time on relevant locations and higher on irrelevant locations while bottom ranking sailors make a lowRecurrence time in most of the locations.
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Approximation of the posterior density for diffusion processes
TL;DR: The quality of the resulting approximation to the exact but intractable posterior is addressed and it is proved under global assumptions the weak convergence of the approximate posterior to the true posterior as the number of intermediate points used in the Euler scheme grows to infinity.
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Integral equation solutions as prior distributions for Bayesian model selection
TL;DR: In many statistical problems we deal with more than one model and the prior information on the parameters of the models is vague default priors are typically used as discussed by the authors, which are usually improper provoking a calibration problem which precludes the comparison of models.