## Recently Published

##### Variational Mixture of Gaussians

Tutorial-like document on using variational inference on a Gaussian mixture model, and show how a Bayesian treatment resolves overfitting issues present in the maximum likelihood approach.

##### Melissa vignette analysis

This document is a vignette for the Melissa package on analysing scBS-seq data and performing Bayesian clustering and imputation of single cell methylomes.

##### Melissa vignette to process files

This document is a vignette for the Melissa package on how to process raw scBS-seq data, which then can be used for the Bayesian clustering and imputation of single cell methylomes.

##### Vignette for BPRMeth package

This document is the vignette for the Bioconductor package BPRMeth

##### Variational Mixture of Bayesian Probit Regressions

Tutorial-like document on using variational approximations to perform Bayesian inference for mixture of Bernoulli Binomial probit regression models.

##### Variational Mixture of Bayesian Linear Regressions

Tutorial-like document on using variational approximations to perform Bayesian inference for mixture of linear regression models.

##### Variational Bayesian Probit Regression

Tutorial-like document on using variational approximations to perform Bayesian inference for Bernoulli/Binomial probit regression models.

##### Variational Bayesian Linear Regression

Tutorial-like document on using variational approximations to perform Bayesian inference for linear regression models.

##### Beta Binomial for overdispersion

Tutorial-like document on Beta-Binomial distribution for modelling overdispersed data, mainly arising when measuring data that have more than one source of variation.

##### Bayesian Binomial Probit Regression (BPR) Model

Tutorial-like document on how to perform Bayesian Binomial probit regression using the data augmentation approach and also using the MH algorithm to compute the posterior distribution.

##### Bayesian Binary Probit Model

Tutorial-like document on how to perform Bayesian binary probit regression using the data augmentation approach.