# Bayesian Statistics

Data Analytics
Data and AI

Bayesian statistics is a framework based on Bayes’ Theorem, which has revolutionized many industries by dealing with probability distributions differently (as Bayes would say: Common sense expressed in numbers). The Bayesian interpretation of ‘probability’ defines probability as a degree of belief that an event will occur given prior experiences. In this 2-day deep-dive into Bayesian statistics, you’ll discover how to solve multi-armed bandits using techniques such as Markov chain Monte Carlo, Variational Inference, and much more.

## What will you learn?

After the training, you will be able to:

Understand the theory of Bayesian Statistics and how it differs from the classical approach.

Apply Bayes’ Theorem to real-world problems.

Use PyMC to put Bayesian statistics into practice.

## Key takeaways

#### Introduction to Bayes’ Theorem

1. Learn the Bayesian interpretation of probability: what it is and how it differs from frequentist statistics
2. Understand the main components of Bayes Theorem: Priors, Likelihoods, and Posterior Distributions
3. Application of Bayes’ Theorem to solve probabilistic problems

#### Probability distributions

1. Different distributions
2. The difference between probability mass and density functions

#### Introduction to Bayes’ Theorem

1. Translate a real-world problem into a probabilistic model
2. Finding the posterior distribution in practice
3. When to use Markov Chain Monte Carlo and when to use Variational Inference
4. The Random Walk Metropolis-Hastings algorithm and how it works

## Program

• Introduction to Bayesian statistics
• Bayes’ Theorem
• Bayesian Coin Flipping
• Introduction to PyMC3 and distributions
• Bayesian Modelling in practice
• Markov Chain Monte Carlo with PyMC3

## Who is it for?

If you already understand basic statistics and want to learn about Bayesian statistics and how these differ from the frequentist approach, then this training is for you. Learn to solve real-world problems by translating them into probabilistic models.

Requirements

An understanding of the fundamentals of statistics will be required, as well as experience with Python.

Want to learn more about basic statistics concepts and Python first? Then check out our A/B Testing and Innovating Through Experiments course!

## What else should I know?

After registering for this training, you will receive a confirmation email with practical information. A week before the training, we will ask you about any dietary requirements and share literature if you need to prepare.

See you soon!

Course information

All literature and course materials are included in the price.

All literature and course materials are included in the price.

After registering for this course, you will receive a confirmation email with practical information.

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