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Intuitive Bayes/Practical MCMC
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Practical MCMC

  • Course
  • 54 Lessons

Markov Chain Monte Carlo is one of the most utilized tools for modern Bayesian practitioners. It's an algorithm which enables Inference in many settings and models. In this course, we'll provide an in-depth understanding of the algorithm that makes it all possible.
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Contents

Introduction

Get an overview of MCMC, and learn why it's so relevant for applied data scientists and statisticians. Get familiar with our teaching style where we teach intuitive concepts, filled with practical examples and hands-on code.

Welcome to the MCMC Course
  • 2 mins
  • 206 MB
Preview
What do the MC and MC in MCMC stand for?
  • 2 mins
  • 9.12 MB
Preview
What MCMC Enables
  • 2 mins
  • 4.13 MB
Preview
The reality of working with MCMC
  • 3 mins
  • 6.39 MB
Preview
Who's this course for?
  • 3 mins
  • 6.12 MB
Preview
What we'll cover
  • 3 mins
  • 10.3 MB
Preview
Lesson Feedback
    Lesson Exercises

      Resources Library

      Instructions on how access the various resources provided in this course

      Intuitive Bayes Discourse Community Invite
        Github Repository and Code Access
          Environment Installation with Anaconda
          • 13 mins
          • 247 MB
          Preview
          Optional Orientation: Github
          • 2 mins
          • 2.98 MB
          Preview
          Optional Orientation: Discourse
          • 2 mins
          • 4.45 MB
          Preview
          Optional Orientation: Podia
          • 4 mins
          • 10.8 MB
          Preview

          Investigating Inference

          Learn the different ways to perform inference, from basic conjugate methods to grid search and their limitations. Then you'll understand why MCMC is a go to tool for today's practitioners.

          Introduction
          • 3 mins
          • 3.72 MB
          Basic Bayes
          • 11 mins
          • 20.4 MB
          Conjugate Models
          • 7 mins
          • 18.8 MB
          Grid Search
          • 12 mins
          • 20.9 MB
          Lesson Recap
          • 2 mins
          • 3.21 MB
          Lesson Feedback
            Lesson Exercises
              Lesson References

                Markov Chain Monte Carlo Deep Dive

                Build your own sampler, before moving onto more modern variants such as Hamiltonian Monte Carlo. Learn how MCMC works, and where it sometimes doesn't.
                Introduction
                • 3 mins
                • 6.6 MB
                MCMC Deep Dive
                • 6 mins
                • 20.3 MB
                Introducing Metropolis Hastings
                • 13 mins
                • 35.1 MB
                Introducing Hamiltonian Monte Carlo
                • 8 mins
                • 23.1 MB
                MCMC in Practice
                • 12 mins
                • 34.5 MB
                Lesson Recap
                • 4 mins
                • 17.5 MB
                Lesson Feedback
                  Lesson Exercises
                    Lesson References

                      The MCMC Practioners Toolbox

                      MCMC is paired with complementary tools such as numerical and visual diagnostics. Learn what these tools are, when to use them, and how to interpret their outputs so you can be confident of the results.
                      Introduction
                      • 6 mins
                      • 21.5 MB
                      Diagnostics intuitions
                      • 9 mins
                      • 25.3 MB
                      Trace Plots
                      • 8 mins
                      • 34.4 MB
                      Rank Plots
                      • 8 mins
                      • 20.7 MB
                      R Hat
                      • 5 mins
                      • 18.4 MB
                      Autocorrelation and effective size
                      • 10 mins
                      • 25 MB
                      Divergences
                      • 8 mins
                      • 27 MB
                      Diagnostics in an end-to-end Bayesian workflow
                      • 6 mins
                      • 24.7 MB
                      Lesson Recap
                      • 5 mins
                      • 13.7 MB
                      Lesson Feedback
                        Lesson Exercises
                          Lesson References

                            Not So Random Topics

                            Practical tips for working with MCMC, such as model reparameterization, sampler tuning, and setting priors to get the best results from your sampler.

                            Introduction
                            • 4 mins
                            • 10.6 MB
                            Practical HMC Tuning
                            • 18 mins
                            • 72.4 MB
                            (Hierarchical) Reparamaterization
                            • 11 mins
                            • 37.6 MB
                            Changing the data
                            • 11 mins
                            • 38.4 MB
                            A Cornucopia of Samplers
                            • 11 mins
                            • 52.4 MB
                            Monte Carlo Standard Error
                            • 18 mins
                            • 63.2 MB
                            Lesson Recap
                            • 5 mins
                            • 13.9 MB
                            Lesson Feedback
                              Lesson Exercises
                                Lesson References

                                  Final Notes

                                  Congratulations!
                                  • 2 mins
                                  • 147 MB