In this section we briefly consider parameter estimation by maximum likelihood, as first devised by Fisher (1956). While in the foregoing we used a finite sample space, we now employ a space with infinitely many possible samples. Accordingly, a probability distribution over sample space is written down in terms of a so-called density function, denoted \(P(s) ds\), which technically speaking expresses the infinitely small probability assigned to an infinitely small patch \(ds\) around the point \(s\). This probability density works much like an ordinary probability function.