Non linear Mixed Effects Models: Bridging the gap between Independent Metropolis Hastings and Variational Inference

EasyChair Preprint no. 349

Abstract

Variational inference and MCMC methods have been two popular methods in order to sample from a posterior distribution. Whereas the former extends the computation feasibility to higher dimension, the latter takes advantage of nice convergence properties to the exact posterior distribution. In this work we'll draw the parallel between a famous MCMC scheme called the Independent Metropolis Hastings and Variational inference. We'll explain our work on both Linear and Non-linear Gaussian cases. In the non linear case, a new proposal will be introduced motivated by a faster convergence of the Markov chain.

Keyphrases: inference, MCMC, mixed effects, Variational

@Booklet{EasyChair:349,
  author = {Belhal Karimi},
  title = {Non linear Mixed Effects Models: Bridging the gap between Independent Metropolis Hastings and Variational Inference},
  howpublished = {EasyChair Preprint no. 349},
  doi = {10.29007/r7wq},
  year = {EasyChair, 2018}}