Mixture models may be a useful and flexible tool to describe data with a complicated structure, for instance characterized by multimodality or asymmetry. In a Bayesian setting, it is a well established fact that one need to be careful in using improper prior distributions, since the posterior distribution may not be proper. This feature leads to problems in carry out an objective Bayesian approach. In this work an analysis of Jeffreys priors in the setting of finite mixture models will be presented.
Jeffreys priors for mixture models / Grazian, Clara; C. P., Robert. - ELETTRONICO. - (2014). (Intervento presentato al convegno 47th Scientific Meeting of the Italian Statistical Society tenutosi a Cagliari nel 11-13/06/2014).
Jeffreys priors for mixture models
GRAZIAN, CLARA;
2014
Abstract
Mixture models may be a useful and flexible tool to describe data with a complicated structure, for instance characterized by multimodality or asymmetry. In a Bayesian setting, it is a well established fact that one need to be careful in using improper prior distributions, since the posterior distribution may not be proper. This feature leads to problems in carry out an objective Bayesian approach. In this work an analysis of Jeffreys priors in the setting of finite mixture models will be presented.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
