has_specified_output data transformation model parameter estimation model parameter estimate maximum likelihood estimation Philippe Rocca-Serra http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html https://en.wikipedia.org/wiki/Maximum_likelihood_estimation maximum likelihood estimation (MLE) is a method of estimating the parameters of a statistical model, given observations. MLE attempts to find the parameter values that maximize the likelihood function, given the observations. The method of maximum likelihood is based on the likelihood function, {\displaystyle {\mathcal {L}}(\theta \,;x)} {\displaystyle {\mathcal {L}}(\theta \,;x)}. We are given a statistical model, i.e. a family of distributions {\displaystyle \{f(\cdot \,;\theta )\mid \theta \in \Theta \}} {\displaystyle \{f(\cdot \,;\theta )\mid \theta \in \Theta \}}, where {\displaystyle \theta } \theta denotes the (possibly multi-dimensional) parameter for the model. The method of maximum likelihood finds the values of the model parameter, {\displaystyle \theta } \theta , that maximize the likelihood function, {\displaystyle {\mathcal {L}}(\theta \,;x)} {\displaystyle {\mathcal {L}}(\theta \,;x)}. I ready for release