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SUMMARY:Bayesian formulation and implementation for a non-stationary semi-
 Markov model with covariates
DTSTART:20251104T101500Z
DTEND:20251104T111500Z
DTSTAMP:20260504T170900Z
UID:indico-event-14454@indico.math.cnrs.fr
DESCRIPTION:Speakers: Sebastien Coube (Inrae\, Unité MIAT)\n\nModeling th
 e impact of co-variates upon the transition from one hidden state to anoth
 er may improve both estimation and prediction in hidden hidden Markov and 
 semi-Markov models. However\, the problem of the interpretability of the p
 arametrization and feasability of the computation becomes crippling as the
  number of interest covariates increases. I propose\, in a Bayesian perspe
 ctive\, an architecture I hope to be as complex as needed to accommodate c
 omplex configurations of the data\, but as simple as possible for the sake
  of usability. A few simulated examples are given to illustrate the flexib
 ility of the model. A Bayesian strategy to fit the model is discussed.(Wor
 k in progress)\n\nhttps://indico.math.cnrs.fr/event/14454/
LOCATION:Salle K. Johnson (1R3\, 1er étage)
URL:https://indico.math.cnrs.fr/event/14454/
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