Distributed Bayesian Inference in Linear Mixed-Effects Models

Bayesian Inference for Spatial Beta Generalized Linear Mixed Models

In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...

Bayesian inference for generalized linear mixed models.

Generalized linear mixed models (GLMMs) continue to grow in popularity due to their ability to directly acknowledge multiple levels of dependency and model different data types. For small sample sizes especially, likelihood-based inference can be unreliable with variance components being particularly difficult to estimate. A Bayesian approach is appealing but has been hampered by the lack of a ...

Approximate Bayesian Inference in Spatial Generalized Linear Mixed Models

In this paper we propose fast approximate methods for computing posterior marginals in spatial generalized linear mixed models. We consider the common geostatistical special case with a high dimensional latent spatial variable and observations at only a few known registration sites. Our methods of inference are deterministic, using no random sampling. We present two methods of approximate infer...

Bayesian inference for mixed effects models with heterogeneity

We are interested in Bayesian modelling of panel data using a mixed e ects model with heterogeneity in the individual random e ects. We compare two di erent approaches for modelling the heterogeneity using a mixture of Gaussians. In the rst model, we assume an in nite mixture model with a Dirichlet process prior, which is a non-parametric Bayesian model. In the second model, we assume an over-p...

Bayesian variable selection in linear mixed effects models