Eliciting priors with Neural Networks: an example estimating COVID-19 evolution in Spain
S. Cabras
By using Neural Networks for eliciting priors on Poisson means, we propose a semi-parametric approach to estimate the evolution of COVID-19 (SARS-CoV-2) in Spain. Considering the sequences of 14-day cumulative incidence of all Spanish regions, it combines modern Deep Learning (DL) techniques for analyzing sequences with the usual Bayesian Poisson-Gamma model for counts. The DL model provides a suitable description of the observed time series of counts, but it cannot give a reliable uncertainty quantification. The role of expert elicitation of the expected number of counts and its reliability is DL predictions' role in the proposed modelling approach. Finally, the posterior predictive distribution of counts is obtained in a standard Bayesian analysis using the well known Poisson-Gamma model. The model allows to predict the future evolution of the sequences on all regions or estimates the consequences of eventual scenarios.
Keywords: Applied Bayesian methods; COVID-19; Deep Learning; Multivariate Time Series; LSTM; SARS-CoV-2
Scheduled
GT21 Bayesian Inference I
June 7, 2022 12:00 PM
Audiovisual room
Other papers in the same session
R. Jiménez Llamas, E. CARRIZOSA, P. Ramirez Cobo
R. Cuenca De Armas, M. T. Balaguer-Coll, L. Caldirola, D. V. Conesa Guillén, E. Tortosa-Ausina
A. López-Quílez, M. Marco, E. Gracia, M. Lila
C. Armero Cervera, F. Llopis-Cardona, G. Sanfélix-Gimeno
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