Who's this course for?

Who's this course for?

Who's this course for?

How this course is targeted for applied practitioners and what that means for the content we'll showcase, and how we'll cover it

Practical MCMC

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Introduction

Welcome to the MCMC Course

What do the MC and MC in MCMC stand for?

What MCMC Enables

The reality of working with MCMC

Who's this course for?

What we'll cover

Lesson Feedback

Lesson Exercises

Resources Library

Intuitive Bayes Discourse Community Invite

Github Repository and Code Access

Environment Installation with Anaconda

Optional Orientation: Github

Optional Orientation: Discourse

Optional Orientation: Podia

Investigating Inference

Introduction

Basic Bayes

Conjugate Models

Grid Search

Lesson Recap

Lesson Feedback

Lesson Exercises

Lesson References

Markov Chain Monte Carlo Deep Dive

Introduction

MCMC Deep Dive

Introducing Metropolis Hastings

Introducing Hamiltonian Monte Carlo

MCMC in Practice

Lesson Recap

Lesson Feedback

Lesson Exercises

Lesson References

The MCMC Practioners Toolbox

Introduction

Diagnostics intuitions

Trace Plots

Rank Plots

R Hat

Autocorrelation and effective size

Divergences

Diagnostics in an end-to-end Bayesian workflow

Lesson Recap

Lesson Feedback

Lesson Exercises

Lesson References

Not So Random Topics

Introduction

Practical HMC Tuning

(Hierarchical) Reparamaterization

Changing the data

A Cornucopia of Samplers

Monte Carlo Standard Error

Lesson Recap

Lesson Feedback

Lesson Exercises

Lesson References

Final Notes

Congratulations!