Ivan Palomares Carrascosa covers the concept of Bayesian regression:
In this article, you will learn:
- The fundamental difference between traditional regression, which uses single fixed values for its parameters, and Bayesian regression, which models them as probability distributions.
- How this probabilistic approach allows the model to produce a full distribution of possible outcomes, thereby quantifying the uncertainty in its predictions.
- How to implement a simple Bayesian regression model in Python with scikit-learn.
My understanding is that both Bayesian and traditional regression techniques get you to (roughly) the same place, but the Bayesian approach makes it harder to forget that the regression line you draw doesn’t actually exist and everything has uncertainty.