**O**n the last day of the IFCAM workshop in Bangalore, Marc Lavielle from INRIA presented a talk on mixed effects where he illustrated his original computer language Monolix. And mentioned that his CRC Press book on *Mixed Effects Models for the Population Approach* was out! (Appropriately listed as out on a 14th of July on amazon!) He actually demonstrated the abilities of Monolix live and on diabets data provided by an earlier speaker from Kolkata, which was a perfect way to start initiating a collaboration! Nice cover (which is all I saw from the book at this stage!) that maybe will induce candidates to write a review for CHANCE. Estimation of those mixed effect models relies on stochastic EM algorithms developed by Marc Lavielle and Éric Moulines in the 90’s, as well as MCMC methods.

## Archive for mixed effect models

## Bangalore workshop [ಬೆಂಗಳೂರು ಕಾರ್ಯಾಗಾರ] and new book

Posted in Books, pictures, R, Statistics, Travel, University life with tags Bangalore, book review, CHANCE, EM, IFCAM, Indian Institute of Science, INRIA, Kolkata, Marc Lavielle, MCMC, mixed effect models, Monolix, SAEM on August 13, 2014 by xi'an## Computing evidence

Posted in Books, R, Statistics with tags Bayesian model choice, evidence, harmonic mean estimator, latent variable, Lecture Notes in Statistics, MCMC, mixed effect models, path sampling, prior projection, simulation, unbiasedness on November 29, 2010 by xi'anThe 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)

when

- is the model index,
- the ‘s are simulated from the posterior under model
*h,* - the model only considers the
*h-1*first components of , - 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**