Archive for Lecture Notes in Statistics

Computing evidence

Posted in Books, R, Statistics with tags , , , , , , , , , , on November 29, 2010 by xi'an

The book Random effects and latent variable model selection, edited by David Dunson in 2008 as a Springer Lecture Note. contains several chapters dealing with evidence approximation in mixed effect models. (Incidentally, I would be interested in the story behind the  Lecture Note as I found no explanation in the backcover or in the preface. Some chapters but not all refer to a SAMSI workshop on model uncertainty…) The final chapter written by Joyee Ghosh and David Dunson (similar to a corresponding paper in JCGS) contains in particular the interesting identity that the Bayes factor opposing model h to model h-1 can be unbiasedly approximated by (the average of the terms)



  • \mathfrak{M} is the model index,
  • the \theta_{i,h}‘s are simulated from the posterior under model h,
  • the model \mathfrak{M}=h-1 only considers the h-1 first components of \theta_{i,h},
  • the prior under model h-1 is the projection of the prior under model h. (Note that this marginalisation is not the projection used in Bayesian Core.)

Continue reading