In this article, we’ll derive from scratch the well known formulas –and some not so well known ones– for nonlinear least squares fitting from a Bayesian perspective. We’ll be using only elementary linear algebra and elementary calculus. It turns out, that this is a valuable exercise, because it allows us to clearly state our assumptions about the problem and assign unambigous meaning to all components of the least squares fitting process.
