The goal of a machine learning regression problem is to predict a single numeric value. There are roughly a dozen different regression techniques such as basic linear regression, k-nearest neighbors ...

The main focus of this short course will be the Bayesian aspect of it. That means this is a slightly more advanced course requiring some knowledge of basic probability, regression methods, and the R ...

Bayesian quantile regression and statistical modelling represent a growing paradigm in contemporary data analysis, extending conventional regression by estimating various conditional quantiles rather ...

Bayesian inference provides a flexible way of combining data with prior information. However, quantile regression is not equipped with a parametric likelihood, and therefore, Bayesian inference for ...

Multivariate meta-regression models are commonly used in settings where the response variable is naturally multidimensional. Such settings are common in cardiovascular and diabetes studies where the ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the naive Bayes regression technique, where the goal is to predict a single numeric value. Compared to other ...