[Forum SIS] Fwd: Webinar DAVID ROSSELL

[Pierpaolo De Blasi]

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 a unito.it>
Date: lun 14 dic 2020 alle ore 12:32
Subject: Webinar DAVID ROSSELL
To: <sis a stat.unipg.it>


WEBINARS IN STATISTICS @ COLLEGIO CARLO ALBERTO
<https://www.carloalberto.org/events/category/seminars/seminars-in-statistics/>


Joint initiative with


MIDAS COMPLEX MODELING RESEARCH NETWORK <http://midas.mat.uc.cl/network>



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



------------------------------------------------

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


Title: *Approximate Laplace approximation*


Zoom link:

https://us02web.zoom.us/j/84312531120?pwd=VVJraStLVkR2M0w0ZXZnU3M0MFp2UT09
<https://us02web.zoom.us/j/88252069649?pwd=V2Z3b1UrZVVWNWZ4OXhydUtIakxpUT09>

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.

------------------------------------------------


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


---

University of Torino & Collegio Carlo Alberto

carloalberto.org/pdeblasi
<https://sites.google.com/a/carloalberto.org/pdeblasi/>
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