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pro vyhledávání: '"Pozza, Francesco"'
Autor:
Pozza, Francesco, Zanella, Giacomo
We study multiproposal Markov chain Monte Carlo algorithms, such as Multiple-try or generalised Metropolis-Hastings schemes, which have recently received renewed attention due to their amenability to parallel computing. First, we prove that no multip
Externí odkaz:
http://arxiv.org/abs/2410.23174
Routinely-implemented deterministic approximations of posterior distributions from, e.g., Laplace method, variational Bayes and expectation-propagation, generally rely on symmetric approximating densities, often taken to be Gaussian. This choice faci
Externí odkaz:
http://arxiv.org/abs/2409.14167
Gaussian approximations are routinely employed in Bayesian statistics to ease inference when the target posterior is intractable. Although these approximations are asymptotically justified by Bernstein-von Mises type results, in practice the expected
Externí odkaz:
http://arxiv.org/abs/2301.03038
Akademický článek
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Supplemental material, sj-pdf-2-smm-10.1177_09622802231167436 for Improved and computationally stable estimation of relative risk regression with one binary exposure by Francesco Pozza, Euloge Clovis Kenne Pagui and Alessandra Salvan in Statistical M
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e447c70751da12dca11a75f43e1c5426