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Scalable statistical inference with INLA

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SINW01 - Scalable statistical inference

INLA do approximate Bayesian inference for the class of latent Gaussian models. It has shown sucessful allowing statisticians and applied scientists to fast and reliable Bayesian inference for a huge class of additve models, within reasonable time. Especially, the use of spatial Gaussian models using the SPDE approach has been particularly popular. Although most models runs within reasonable time, we are facing with the current implementation, limitations for really huge models like large space time models. In this talk I will discuss the current situation and possible strategies to improve the situation.

This talk is part of the Isaac Newton Institute Seminar Series series.

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