Take your first steps in the Bayesian world. In this chapter, you’ll be introduced to the basic concepts of probability and statistical distributions, as well as to the famous Bayes' Theorem, the cornerstone of Bayesian methods. Finally, you’ll build your first Bayesian model to draw conclusions from randomized coin tosses.

Who is Bayes? What is Bayes?

Bayesians vs. Frequentists

Probability distributions

Probability and Bayes' Theorem

Let's play cards

Bayesian spam filter

What does the test say?

Tasting the Bayes

Tossing a coin

The more you toss, the more you learn

Hey, is this coin fair?

The Bayesian way

It’s time to look under the Bayesian hood. You’ll learn how to apply Bayes' Theorem to drug-effectiveness data to estimate the parameters of probability distributions using the grid approximation technique, and update these estimates as new data become available. Next, you’ll learn how to incorporate prior knowledge into the model before finally practicing the important skill of reporting results to a non-technical audience.

Under the Bayesian hood

Towards grid approximation

Grid approximation without prior knowledge

Updating posterior belief

Prior belief

The truth of the prior

Picking the right prior

Simulating posterior draws

Reporting Bayesian results

Point estimates

Highest Posterior Density credible intervals

The meaning of credibility

Bayesian estimation

Apply your newly acquired Bayesian data analysis skills to solve real-world business challenges. You’ll work with online sales marketing data to conduct A/B tests, decision analysis, and forecasting with linear regression models.

A/B testing

Simulate beta posterior

Posterior click rates

A or B, and how sure are we?

How bad can it be?

Decision analysis

Decision analysis: cost

Decision analysis: profit

Regression and forecasting

Defining a Bayesian regression model

Analyzing regression parameters

Predictive distribution

Bayesian inference

In this final chapter, you’ll take advantage of the powerful PyMC3 package to easily fit Bayesian regression models, conduct sanity checks on a model's convergence, select between competing models, and generate predictions for new data. To wrap up, you’ll apply what you’ve learned to find the optimal price for avocados in a Bayesian data analysis case study. Good luck!

Markov Chain Monte Carlo and model fitting

Markov Chain Monte Carlo

Sampling posterior draws

Interpreting results and comparing models

Inspecting posterior draws

Comparing models with WAIC

Making predictions

Sample from predictive density

Estimating test error

How much is an avocado?

Fitting the model

Inspecting the model

Optimizing the price

Final remarks

Bayesian linear regression with pyMC3

Ads Data

Bikes Data

Bayesian data analysis is an increasingly popular method of statistical inference, used to determine conditional probability without having to rely on fixed constants such as confidence levels or p-values. In this course, you’ll learn how Bayesian data analysis works, how it differs from the classical approach, and why it’s an indispensable part of your data science toolbox. You’ll get to grips with A/B testing, decision analysis, and linear regression modeling using a Bayesian approach as you analyze real-world advertising, sales, and bike rental data. Finally, you’ll get hands-on with the PyMC3 library, which will make it easier for you to design, fit, and interpret Bayesian models.

Introduction to Statistics in Python

Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!

Bayesian Data Analysis in Python

Statistiques appliquées en Python

Machine Learning Engineer

Running the model revisited

Trace plot

Trace plot: zoom in on one parameter

Forest plot

Trace summary

Fitting another model

Widely Applicable Information Criterion (WAIC)

Compare plot

Let's practice comparing models!

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Bayesian linear regression with pyMC3 - Interpreting results and comparing models

Interpreting results and comparing models

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