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# Take a deep dive into Bayesian modeling.

Bayesian statistics is a theory based on Bayesian probability, an idea that has revolutionized many industries by dealing with probability distributions in a different way. The Bayesian interpretation of probability expresses probability as a degree of belief in an event. 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 and Variational Inference, and much more.

### This training is for you if…

• You already understand basic statistics.
• You are interested in learning about Bayesian statistics and how it differs from the frequentist approach.
• You want to solve real-world problems by translating them into probabilistic models.

### This training is not for you if…

• You are missing the fundamentals of statistics.
• You are NOT interested in improving the way you work with statistics.
• You have never used Python before.

### What you'll learn

#### Bayes' Theorem

• The Bayesian interpretation of probability: what it is and how it differs from classical interpretations
• Prior, likelihood, and posterior distributions
• Application of Bayes’ Theorem to solve probabilistic problems

#### Probability Distribution

• Different distributions
• The difference between probability mass and density functions

#### Bayes' Theorem in Practice

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

## The schedule

Day 1: Theory and Hands-On Labs
• Fundamentals: Bayes’ Theory
• From Bayes’ Theorem to Bayesian Data Analysis
• Markov chain Monte Carlo with PyMC3
• Variational Inference: Big Data Bayesian Data Analysis

Day 2: Hackathon: Multi-Armed Bandits with the Bayesian approach

Multi-armed bandit problems (like for example A/B testing) can be solved by using Bayesian modeling. Participants will be presented with the simulation environment for Multi-armed bandits and encouraged to code a Bayesian decision-making algorithm. A perfect opportunity to creatively brainstorm and learn more about practical applications of Bayesian theory and effectively balancing the exploration-exploitation tradeoff

## 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.

## This Bayesian Modeling training is perfect for

1. Data Scientists who know Machine Learning and want to learn about Bayesian statistics.
2. This training is especially suited for Data Scientists who want to go beyond the standard probability theory.
3. To get the most out of this training, we advise that you have at least one year of working experience with Python.

## What will you learn during the Bayesian Modeling training:

1. You will understand what makes Bayesian Probability so powerful, especially compared to the traditional frequentist approach.
2. You will learn how to use the PyMC for building Bayesian models.
3. We will also teach you how to apply Markov Chain Monte Carlo, Variational inference and other applications of Bayesian modeling in practice.

Data Enchanter

Vadim is Data Scientist passionate about solving data-driven problems and sharing his analytical insights to make Data literacy a reality for all.

Flexible delivery

## The Right Format For Your Preferred Learning Style

In-Classroom & In-Company Training
Online, Instructor-Led Training
Hybrid and Blended Learning
Self-Paced Training
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## Have any questions?

Contact Giovanni Lanzani, our Managing Director of Learning and Development, if you want to know more. He’ll be happy to help you!

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You can reach him out by phone as well at +31 6 51 20 6163

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