Archive for R-INLA

ISBA on INLA [webinar]

Posted in R, Statistics, University life with tags , , , , , , on April 3, 2013 by xi'an

If you have missed the item of information, Håvard Rue is giving an ISBA webinar tomorrow on INLA:

the ISBA Webinar on INLA is scheduled for April 4th, 2013
from 8:30 - 12:30 EDT.

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To join the online meeting (Now from mobile devices using the Cisco WebEx
Meeting App)
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1. Go to  https://www.webex.com/login/attend-a-meeting
2. Enter the meeting number  730 293 070 and click Join Now
3. Enter your name and email address, the meeting password and
click "Join Now"

A recording of the webinar will be provided shortly after the event.

Please verify that your computer is capable of connecting using WebEx at

https://support.webex.com/MyAccountWeb/systemRequirement.do?root=Tools&parent=System

or see https://www.webex.com/login/join-meeting-tips  if you are having
trouble connecting.

Latent Gaussian Models im Zürich [day 1]

Posted in R, Statistics with tags , , , , , on February 5, 2011 by xi'an

An interesting first day (for me) at the Latent Gaussian Models workshop in Zürich. The workshop is obviously centred at the INLA approach, with Havard Rue giving a short course on Wednesday then a wide ranging tour of the applications and extensions of INLA this afternoon. Thanks to his efforts in making the method completely accessible for many models through an R package, using mode description commands like

inla(formula, family="weibull", data=Kidney, control.inla=list(h=0.001))

there is now a growing community of INLA users. As exemplified by the attendees to this workshop. Chris Holmes gave another of his inspirational talks this afternoon when defending the use of quasi-Monte Carlo methods in Bayes factor approximations. The model choice session this morning showed interesting directions, including a calibration of the Hellinger distance by Bernoulli distributions, while the application session this afternoon covered owls, bulls, and woolly mammoths. I even managed to speak about ABC model choice, Gaussian approximations of Ising models, stochastic volatility modelling, and grey codes for variable selection, before calling it a (full and fruitful) day!

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