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UID:2535@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20181029T110000
DTEND;TZID=Europe/Paris:20181029T120000
DTSTAMP:20181014T090000Z
URL:https://www.i2m.univ-amu.fr/evenements/non-informative-and-weakly-info
 rmative-bayesian-priors/
SUMMARY: (...): Non-informative and weakly informative Bayesian priors
DESCRIPTION:: Priors play a key role in Bayesian modelling\, computation an
 d inference. There is interest in the formulation of so-called uninformati
 ve or weakly informative priors\, which carry little to no information in 
 the posterior distribution\, given the data. Although there has been a sub
 stantial amount of research on these types of priors\, the increasing comp
 lexity of models and the expansion of computational algorithms motivates n
 ew ideas and insights. In this presentation\, I will discuss some recent r
 esearch into the formulation of such priors for mixture models\, hypothesi
 s testing and model evaluation. The integral role of approximate Bayesian 
 computation (ABC) as a computational tool will also be highlighted. This w
 ork is joint with a number of co-authors\, listed below. -References:-Z va
 n Havre\, N White\, J Rousseau\, K Mengersen (2015)  Overfitting Bayesian 
 Mixture Models with an Unknown Number of Components. PLoS One. 10\, 1-27.-
 K Kanary\, K Mengersen\, CP Robert\, J Rousseau (2018) Testing hypotheses 
 via a mixture estimation model. arXiv:1412.2044.-DJ Nott\, CC Drovandi\, K
  Mengersen\, M Evans (2018) Approximation of Bayesian predictive p-values 
 with regression ABC. Bayesian Analysis. 13\, 59-83.-J Rousseau\, K Mengers
 en (2011) Asymptotic behaviour of the posterior distribution in overfitted
  mixture models. JRSS Series B. 73\, 689–710-W Xueou\, DJ Nott\, CC Drov
 andi\, K Mengersen\, M Evans (2018) Using history matching for prior choic
 e. Technometrics. To appear.http://staff.qut.edu.au/staff/mengerse/
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