Statistical Modeling

Statistical modeling is one of the many branches of mathematics whose aim is to represent phenomena, in this case random ones. This branch is divided into two sub-categories: parametric modeling and non-parametric modeling. Each of these adopts a distinct approach, which leads us, in this journey, to approach each problem in these two forms. In any case, it is essential to know this chapter to better understand machine learning.

📄️ Introduction

Statistical learning is based on "well-formed"statistical models. This makes it easy to define likelihood.

📄️ Estimators

We create estimators from sufficient statistics. These estimators have some good properties and sometimes we can even know if they are the best.

📄️ Tests and Confidence Intervals

Test or Confidence Interval is a way to estimate the error we do in our estimation because of the lack of data.

📄️ Appendix

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