Bayesian quantile regression and unsupervised learning methods to the US Army and Navy data

We apply the Bayesian quantile regression (BayesQR) model for binary response variables and unsupervised learning methods to synthetic data (Stevens Anderson-Cook, 2017a, 2017b), which is univariate with a of passing or failing complex munitions generated match age usage rate found in US Department Defense systems (Army Navy). Instead generalised linear (GLM) used Stevens Anderson-Cook (2017a), we propose BayesQR predict Army Navy as well methods. First, want find best models through comparing statistical inference GLMs calculating their percentage correctly classified (PCC) tests accuracy prediction. The second method focuses on clustering using k-means random forests based results BayesQR. compare different covariates one that can divide into two groups: Navy.