[ABE-L] Ciclo de Palestras=?ISO-8859-1?Q?_do_PPG_em_Estat=EDs?=tica (=?ISO-8859-1?Q?15_de_mar=E7o?=)

[Fernando A. S. Moura]

Caros,
 
 Nesta  quarta-feira, dia 15 de março, excepcionalmente  às 13:30hs no 
Laboratório de Sistemas Estocásticos (LSE), sala I-044b, o Professor 
Havard Hue (KAUST)  dará  uma palestra pelo Ciclo de Palestras do PPG em 
Estatística.
 
 Os detalhes seguem abaixo.
 
Mais informações sobre o Ciclo de Palestras do PPG em Estatística 
podem ser encontradas no site www2.dme.ufrj.br.
 
Ciclo de Palestras do PPG em Estatística - IM-UFRJ
 
Data: 15 de março de 2017 
Hora: 13:30
Local: LSE
 
Palestrante:  Havard Hue (KAUST)

Título: Penalising model component complexity: A principled practical 
approach to constructing priors 

Setting prior distributions on model parameters is the act of 
characterising the nature of our uncertainty and has proven a critical 
issue in applied Bayesian statistics. Although the prior distribution 
should ideally encode the users’ uncertainty about the parameters, this 
level of knowledge transfer seems to be unattainable in practice and 
applied statisticians are forced to search for a “default” prior. Despite 
the development of objective priors, which are only available explicitly 
for a small number of highly restricted model classes, the applied 
statistician has few practical guidelines to follow when choosing the 
priors. An easy way out of this dilemma is to re-use prior choices of 
others, with an appropriate reference.
In this talk, I will introduce a new concept for constructing prior 
distributions. We exploit the natural nested structure inherent to many 
model components, which defines the model component to be a flexible 
extension of a base model. Proper priors are defined to penalise the 
complexity induced by deviating from the simpler base model and are 
formulated after the input of a user-defined scaling parameter for that 
model component, both in the univariate and the multivariate case. These 
priors are invariant to reparameterisations, have a natural connection to 
Jeffreys’ priors, are designed to support Occam’s razor and seem to have 
excellent robustness properties, all which are highly desirable and allow 
us to use this approach to define default prior distributions. Through 
examples and theoretical results, we demonstrate the appropriateness of 
this approach and how it can be applied in various situations, like random 
effect models, spline smoothing, disease mapping, Cox proportional hazard 
models with time-varying frailty, spatial Gaussian fields and multivariate 
probit models, etc. Further, we show how to control the overall variance 
arising from many model components in hierarchical models.
This is joint work with a lot of people related to the R-INLA project, and 
is still work in progress.


 Atenciosamente
 Fernando Moura