TY  - CHAP
TI  - Bayesian regularization in latent variable models through shrinkage priors
AB  - Bayesian methods are nowadays widely accepted among statisticians and econometricians, mostly because Bayesian inference in combination with
MCMC methods allows great flexibility in modelling complex data. However, many researchers feel uneasy about choosing priors, because they
want to be as objective as possible. In the hope to let the data speak themselves they try to identify non-informative priors. The present talk gives
some reasons why it is often advantageous to use carefully selected informative priors instead of non-informative ones, in particular in cases where the likelihood function is highly non-regular. Examples include Bayesian inference for finite mixture models when the number of components is unknown and variable selection problems in latent variable models.
AF  - CFE 2011, 5th CSDA International Conference on Computational and Financial Econometrics
PP  - Senate House, University of London
UR  - http://www.cfe-csda.org/cfe11/London2011BoA.pdf
PY  - 2011-12-01
AU  - Frühwirth-Schnatter, Sylvia 
ER -