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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.
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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
- Fundamentals: Bayes’ Theory
- From Bayes’ Theorem to Bayesian Data Analysis
- The Bayesian’ Paradigm
- Markov chain Monte Carlo with PyMC3
- Variational Inference: Big Data Bayesian Data Analysis
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.
learning journey
Data Science Learning Journey
This Bayesian Modeling training is perfect for
- Data Scientists who know Machine Learning and want to learn about Bayesian statistics.
- This training is especially suited for Data Scientists who want to go beyond the standard probability theory.
- 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:
- You will understand what makes Bayesian Probability so powerful, especially compared to the traditional frequentist approach.
- You will learn how to use the PyMC for building Bayesian models.
- We will also teach you how to apply Markov Chain Monte Carlo, Variational inference and other applications of Bayesian modeling in practice.