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!

What do the MC and MC in MCMC stand for?

How MCMC is an algorithm but also an enabler for modern Bayesian workflows