The second edition of Data Science from Scratch, First Principles with Python from Joel Grus is here (since the summer of 2019). The first edition of the book came about 4-5 years ago when data science as a field was nascent and majority of Python was in 2.7. There are two aspects to learn data… Continue reading Data Science From Scratch 2nd Edition: Book Review
Category: Statistical Inference
Introduction to Maximum Likelihood Estimation in R – Part 2
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
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