Maximum likelihood is a very general approach developed by R. A. Fisher, when he was an undergrad. In an earlier post, Introduction to Maximum Likelihood Estimation in R, we introduced the idea of likelihood and how it is a powerful approach for parameter estimation. We learned that Maximum Likelihood estimates are one of the most… Continue reading Introduction to Maximum Likelihood Estimation in R – Part 2
Category: Maximum Likelihood Estimation in R
Introduction to Maximum Likelihood Estimation in R – Part 1
The core of statistical inference can thought of situation like this. You have some observed data and you want to understand the actual population that generated the sample data you have. Parameter estimation by MLE One typically models that the observed data is generated by some probability distribution. For the sake of simplicity, let us… Continue reading Introduction to Maximum Likelihood Estimation in R – Part 1