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UID:1837@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20170626T153000
DTEND;TZID=Europe/Paris:20170626T163000
DTSTAMP:20170611T133000Z
URL:https://www.i2m.univ-amu.fr/evenements/variational-inference-for-proba
 bilistic-poisson-pca/
SUMMARY: (...): Variational inference for probabilistic Poisson PCA
DESCRIPTION:: Many application domains such as ecology or genomics have to 
 deal with multivariate non Gaussian observations. A typical example is the
  joint observation of the respective abundances of a set of species in a s
 eries of sites\, aiming to understand the co-variations between these spec
 ies. The Gaussian setting provides a canonical way to model such dependenc
 ies\, but does not apply in general. We consider here the multivariate exp
 onential family framework for which we introduce a generic model with mult
 ivariate Gaussian latent variables. We show that approximate maximum likel
 ihood inference can be achieved via a variational algorithm for which grad
 ient descent easily applies. We show that this setting enables us to accou
 nt for covariates and offsets. We then focus on the case of the Poisson-lo
 gnormal model in the context of community ecology. http://julien.cremerief
 amily.info
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