Scusate per l'inconveniente,
l'annuncio del seminario di David Rossell di oggi pomeriggio, vedi messaggio in calce, conteneva un errore nel link a Zoom, che si risolve 
1) facendo copia e incolla direttamente sul browser 
oppure 
2) utilizzando il seguente link 

https://us02web.zoom.us/j/84312531120?pwd=VVJraStLVkR2M0w0ZXZnU3M0MFp2UT09

Pierpaolo De Blasi

---------- Forwarded message ---------
Da: Pierpaolo De Blasi <pierpaolo.deblasi@unito.it>
Date: lun 14 dic 2020 alle ore 12:33
Subject: Webinar DAVID ROSSELL
To: <random@mail.dm.unipi.it>


WEBINARS IN STATISTICS @ COLLEGIO CARLO ALBERTO


Joint initiative with


MIDAS COMPLEX MODELING RESEARCH NETWORK



Giovedi 17 Dicembre 2020, alle ore 17:00, si terrà  il seguente webinar:



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Speaker: David Rossell (Universitat Pompeu Fabra, Barcelona, Spain)


Title: Approximate Laplace approximation


Zoom link:

https://us02web.zoom.us/j/84312531120?pwd=VVJraStLVkR2M0w0ZXZnU3M0MFp2UT09

Meeting ID: 843 1253 1120

Passcode: 105560




Abstract: 

Bayesian model selection requires an integration exercise in order to assign posterior model probabilities to each candidate model. The computation becomes cumbersome when the integral has no closed-form, particularly when the sample size is large, or the number of models is large. We present a simple yet powerful idea based on the Laplace approximation (LA) to an integral. LA uses a quadratic Taylor expansion at the mode of the integrand and is typically quite accurate, but requires cumbersome likelihood evaluations (for large n) an optimization (for large p). We propose the approximate Laplace approximation (ALA), which uses an Taylor expansion at the null parameter value. ALA brings very significant speed-ups by avoiding optimizations altogether, and evaluating likelihoods via sufficient statistics. ALA is an approximate inference method equipped with strong model selection properties in the family of non-linear GLMs, attaining comparable rates to exact computation. When (inevitably) the model is misspecified the ALA rates can actually be faster than for exact computation, depending on the type of misspecification. We show examples in non-linear Gaussian regression with non-local priors, for which no closed-form integral exists, as well as non-linear logistic, Poisson and survival regression.

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Il webinar è organizzato dalla "de Castro" Statistics Initiative

www.carloalberto.org/stats

in collaborazione con il Collegio Carlo Alberto e rientra nel Complex Data Modeling Research Network

midas.mat.uc.cl/network


Cordiali saluti,

Pierpaolo De Blasi


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University of Torino & Collegio Carlo Alberto

carloalberto.org/pdeblasi