Erlis Ruli
University of Padua
32 Papers
95 Citations
Erlis Ruli is an academic researcher from University of Padua. The author has contributed to research in topics: Bayesian inference & Approximate Bayesian computation. The author has an hindex of 9, co-authored 32 publications.
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Papers
Objective Bayesian inference with proper scoring rules
TL;DR: In this paper, the authors proposed a scoring rule based posterior distribution for the unknown parameter of interest, which can be used to update the information provided by the scoring rule in the SR-posterior distribution.
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Objective Bayesian inference with proper scoring rules
TL;DR: This paper discusses the use of scoring rules in the Bayes formula in order to compute a posterior distribution, named SR-posterior distribution, and its asymptotic normality, and proposes a procedure for building default priors for the unknown parameter of interest that can be used to update the information provided by the scoring rule in the SR-northern distribution.
21
Robust inference for nonlinear regression models from the Tsallis score: application to COVID-19 contagion in Italy.
Paolo Girardi,Luca Greco,Valentina Mameli,Monica Musio,Walter Racugno,Erlis Ruli,Laura Ventura +6 more
- 12 Aug 2020
TL;DR: An approach of robust fitting on nonlinear regression models, both in a frequentist and a Bayesian approach, which can be employed to model and predict the contagion dynamics of COVID‐19 in Italy are discussed.
19
Dynamic psychophysiological correlates of a learning from text episode in relation to reading goals
Sara Scrimin,Elisabetta Patron,Erlis Ruli,Clovis Euloge Kenne Pagui,Gianmarco Altoè,Lucia Mason +5 more
TL;DR: In this article, the authors investigated undergraduates' cardiac activity during an episode of complex learning from text and found a general trend in which students' HRV was greater in the reading phase compared to baseline.
16
Improved Laplace approximation for marginal likelihoods
TL;DR: In this article, the authors proposed an improved Laplace approximation that reduces the asymptotic error of the standard Laplace formula by one order of magnitude, thus leading to third-order accuracy.