Bayesian Foundations For Professionals

A no non-sense course designed to get you started quickly

This applied course is designed for professionals who want to learn the Foundations of Bayesian inference and modeling.

It is designed for the busy practitioner that does not have time to work through lengthy books, theoretical proofs or esoteric examples. Here we present the core ideas in simple to understand language.

This course is for you if you have ever wanted to learn how Bayesian inference works, Bayesian A/B testing, change point detection, conjugacy, Markov Chain Monte Carlo and Bayesian hierarchical models.
Enroll Now - Get Started With Bayesian Foundations for $150 (save $200!)

Video

A Sample of What's Covered

From basics to advanced concepts step-by-step

📚Grounds up development of Bayesian Inference📚

📈 Best practices for selecting priors📈

🧠Hypothesis Testing and Simple Ensembling techniques🧠

👍 Coverage of core Bayesian inferential techniques 👍

👓 Intuitive explanation of Markov Chain Monte Carlo

⚙️ Simple hierarchical model implementation from scratch⚙️

💻 Programming Metropolis and Gibbs Samplers💻

⚡⚡ How to develop fast conjugate samplers ⚡⚡

And much much more ...

  • Github Starter Code

    Learn by doing! Download the code locally or work from a "Binder" notebook directly online.

  • Clear and Well Documented Examples

    Examples focus on clarity and intuition. Batteries and math included

  • 8 Hours of Content

    Professionally edited, designed and developed based on learning best practices to maximize retention and understanding.

Course curriculum

  • 1

    Setup: Getting Started

    • Let's Get Started!

    • Getting started and how to succeed with this course!

    • Where to get the Code?

    • Why Be a Bayesian?

    • Where to begin - Course Outline
  • 2

    Part 1: Bayesian Foundations

    • Motivating Example

    • Bayes Theorem Part 1

    • Bayes Theorem Part 2

    • Bayes Theorem Part 3

    • Programming: Beta Prior with a Binomial Likelihood

    • Exercise - Applying Bayes Theorem

    • Programming: Gamma Prior and Poisson Likelihood

    • Programming: Modularizing Code and Misspecified Priors

    • Prior Distributions

    • Programming: The effect of priors on Posteriors

    • Posterior Distributions Part 1

      FREE PREVIEW

    • Programming: Transforms, MAPs and CIs

      FREE PREVIEW

    • Posterior Distributions Part 2

    • Programming: Combining Priors and Posteriors

    • Posterior Predictive Distributions Part 1

    • Programming: Exploring the Posterior Predictive Distribution

    • Posterior Predictive Distributions Part 2
  • 3

    Part 2: Conjugate Distributions

  • 4

    Part 3: MCMC Samplers

    • Intro to Markov Chain Monte Carlo (MCMC)

    • Review of MCMC Part 1

    • Review of MCMC Part 2

    • Review of MCMC Part 3

    • MCMC Diagnostics Part 1

    • MCMC Diagnostics Part 2

    • Metropolis Sampler: How it Works

    • Metropolis Sampler: Normal Model

      FREE PREVIEW

    • Metropolis Sampler: Poisson-Gamma Model

    • Metropolis Sampler: Poisson-Gamma Model - Code

    • Gibbs Sampler + Normal Model

    • Gibbs Sampler: Why it Works

    • Gibbs Sampler: A Switch Point Model

    • Metropolis and Gibbs Example + Diagnostics Part 1

    • Metropolis and Gibbs Example + Diagnostics Part 2

    • Other Types of Samplers

    • Where to from here?

Enroll Now to Get Started

Limited time offer $150 (Save $200!)

FAQ

  • Who should take this course?

    This course is designed for technical professionals that are seeking to develop a foundational understanding of Bayesian Methods.

  • What are the prequisites for the course?

    A background in statistics, common probability distributions and python programming are helpful for navigating the topics in this course. While they aren’t hard pre-requisites to understanding the material they will certainly make it easier.

  • Do I get a certificate of completion?

    This is a feature that will be available in the future - at the moment we don’t support issuing certificates of completion.

  • What will I be able to do after this course?

    After this course you will have the tools to develop your expertise and explain the ideas at a foundational level. You will be able to speak informatively about Bayesian methods and design your own samplers through the concepts taught in this course.