Latent Gaussian Models in Zürich [day 1]

February 4, 2011
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(This article was first published on Xi'an's Og » R, and kindly contributed to R-bloggers)

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!


Filed under: R, Statistics Tagged: Havard Rue, latent Gaussian models, quasi-Monte Carlo, R, R-INLA, Zurich

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