A Beginner’s Guide to Bayesian Statistics with PyMC3

Demystifying Probability for Data Enthusiasts

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If you’ve ever felt a bit intimidated by the world of statistics, especially Bayesian statistics, you’re not alone. But fear not! In this article, we’ll take a down-to-earth approach to introduce you to Bayesian statistics using the powerful PyMC3 library. No fancy jargon, just a straightforward journey into the world of probabilities.

Getting Started with Bayesian Statistics

Let’s kick things off with a quick overview. Bayesian statistics is all about updating our beliefs based on new evidence. It’s like a continuous learning process where we refine our understanding of the world as we gather more information. PyMC3, a Python library for probabilistic programming, makes the implementation of Bayesian models surprisingly accessible.

Installing PyMC3

Before we dive into the code, make sure you have PyMC3 installed. You can do this with a simple pip command:

pip install pymc3

A Simple Example: Coin Toss

Let’s start with something familiar — a coin toss. In frequentist statistics, we’d estimate the probability of getting heads by conducting a large…