Efficient Bayesian estimation of mixed effects growth models.

Summary

Mixed models, often described as a mixture of fixed and random terms, are used in cases where the data are clustered due to subpopulations, such as sires in genetics trials, years in trials that are conducted annually, assessors in experiments where the person obtaining the measurements may be subjective, individual in experiments that contain measurements in time on traits such as growth (longitudinal studies) and where higher level terms are considered random. A mixed model handles the different sources of error in data by treating the variation as within and between clusters.

Mixed models can be estimated in several established Bayesian software packages, however, the size of the data common in animal trials makes the use of this software impractical. This project sought to develop a module which uses PyMCMC to efficiently estimates parameters of the Bayesian linear mixed model when sample size is large.