Time Series & Bayesian Regression

From Theory to Application: Bayesian Ridge Regression in Action

Bayesian ridge regression stands as a powerful and versatile technique. It is a Bayesian approach to linear regression that offers a unique perspective on modeling and prediction. This approach combines the best of both worlds: the regularization properties of ridge regression and the probabilistic framework of Bayesian inference.

Bayesian Ridge Regression

Before delving into Bayesian ridge regression, let’s first understand the foundation upon which it builds. Ridge regression is a linear regression technique designed to tackle the issue of multicollinearity in predictive modeling. When predictors (features) in a dataset are highly correlated, traditional linear regression models can become unstable and yield unreliable coefficient estimates.

Ridge regression addresses this by introducing a regularization term to the linear regression objective function. This regularization term, often referred to as the L2 penalty, encourages the model to distribute the weights (coefficients) of the predictors more evenly, effectively reducing the impact of multicollinearity.

What sets Bayesian ridge regression apart is its incorporation of Bayesian statistics. Bayesian methods…