  These notes aim at clarifying different strategies to perform linear
regression from given dataset. Methods like the weighted and ordinary least
squares, ridge regression or LASSO are proposed in the literature. The present
article is my understanding of these methods which are, according to me, better
unified in the Bayesian framework. The formulas to address linear regression
with these methods are derived. The KIC for model selection is also derived in
the end of the document.
