This repository is a collection of notebooks about Bayesian Machine Learning. The following links display some of the notebooks via nbviewer to ensure a proper rendering of formulas. Dependencies are ...

We studied theoretical foundations of learning and several important supervised and unsupervised machine learning methods and algorithms including linear model of regression and classification, ...

Statistical decision theory: risk, decision rules, loss and utility functions, Bayesian expected loss, Frequentist risk. Bayesian Inference: Bayes theorem, prior, posterior and predictive ...

The use of linear mixed models (LMMs) in genome-wide association studies (GWAS) is now widely accepted 1 because LMMs have been shown to be capable of correcting for several forms of confounding ...

To this end, a quantile-regression-type structural model named as the quantile MLIRT (Q-MLIRT) model is introduced under the MLIRT framework. The parameters of the proposed model are estimated using ...

Statistical decision theory: risk, decision rules, loss and utility functions, Bayesian expected loss, Frequentist risk. Bayesian Inference: Bayes theorem, prior, posterior and predictive ...