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.

Onder de Bayesiaanse motorkap

Op weg naar grid-approximatie

Rasterbenadering zonder voorkennis

Posterior bijwerken

A-prioriverwachting

De waarheid van de prior

De juiste prior kiezen

Posterior-trekkingen simuleren

Bayesiaanse resultaten rapporteren

Puntschattingen

Highest Posterior Density-credible intervals

De betekenis van geloofwaardigheid

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

Bayesiaanse data-analyse is een steeds populairdere methode voor statistische inferentie, waarmee je voorwaardelijke kansen kunt bepalen zonder te vertrouwen op vaste constanten zoals betrouwbaarheidsniveaus of p-waarden. In deze cursus leer je hoe Bayesiaanse data-analyse werkt, hoe het verschilt van de klassieke aanpak, en waarom het een onmisbaar onderdeel is van je data science-gereedschapskist. Je gaat aan de slag met A/B-testen, beslissingsanalyse en lineaire regressiemodellen met een Bayesiaanse aanpak terwijl je echte gegevens over advertenties, verkoop en fietsverhuur analyseert. Tot slot ga je hands-on aan de slag met de PyMC3-bibliotheek, die het makkelijker maakt om Bayesiaanse modellen te ontwerpen, te fitten en te interpreteren.

Introduction to Statistics in Python

Ontdek de voordelen van Bayesiaanse data-analyse en pas het toe op diverse praktijkgevallen!

Bayesian Data Analysis in Python

Ontdek alle voordelen van Bayesiaanse data-analyse en pas het toe op allerlei echte situaties!

Toegepaste statistiek in Python

De waarheid van de prior

Bayesian Data Analysis in Python

Praktische interactieve oefening