Introduction

Let’s divide the universe of models into two types: statistical and scientific. Both classes of models aims to understand the relationship between a target variable i.e. the y, and a set of features i.e. the **x. **The former aims to find statistically sound relationships based on data while the latter has the property of being able to describe a cause and effect relationship.

All the examples in this series have used statistical models e.g. GLMs to illustrate a concept in Bayesian inference. However, Bayesian inference is just as relevant to building scientific models.

This article will show how to incorporate Bayesian inference to build scientific models and the benefits of doing so.

The content in this article is based on Chapter 16 in [1].

The code to reproduce the results described in this article can be found in this notebook.

Problem

To keep the math simple, let’s imagine we want to build a model to predict a person’s weight given height.

#statistics #machine-learning #data-science #bayesian-statistics #bayesian-inference