A Simple PyMC model

A Simple PyMC model

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Introductory Course

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Welcome

  • Welcome to Intuitive Bayes
  • Course Welcome and Orientation
  • Github Repository and Code Access
  • Intuitive Bayes Discourse Community
  • Course Presurvey
  • Optional Orientation: Github
  • Optional Orientation: Discourse
  • Optional Orientation: Podia

About this course

  • Why We Created This
  • What is Different About This Course
  • Who is This Course For
  • Playmobil vs Lego
  • Comparison to Other Approaches
  • When Does Bayes Work Best
  • Real World Applications
  • Prerequisites and Outline
  • Lesson Summary
  • Lesson References
  • Lesson Feedback Form

How It All Fits Together

  • Lesson Introduction
  • Considering Multiple Solutions
  • Statistics just becomes counting
  • Inside the Magic Machine
  • The Magic of Sampling
  • Doing it in Code
  • Lesson Summary
  • Lesson References
  • Lesson Exercises
  • Beta Lesson Feedback Form

AB Testing Hands On

  • Lesson Introduction
  • Installing PyMC
  • Setting Up The Model
  • Getting the Plausible Values
  • Getting Analytical
  • Putting it All Together
  • Lesson Summary
  • Lesson References
  • Lesson Exercises
  • Lesson Feedback Form

Computational Distributions

  • Lesson Introduction
  • Distributions and Uncertainty
  • Distribution Inputs: Parameters
  • Distribution Outputs: PMF/PDF
  • Two Types of Samples
  • Two Types of Spaces
  • Lesson Recap
  • Lesson References
  • Lesson Exercises
  • Lesson Feedback Form

Bayes Rule

  • Lesson Introduction
  • Prior Distribution
  • Likelihood Distribution
  • Posterior Distribution
  • Prior and Posterior Predictive Distributions
  • Markov Chain Monte Carlo
  • Common Distributions in Modern Bayes
  • Lesson Recap
  • Lesson References
  • Lesson Exercises
  • Lesson Feedback Form

Bayesian Linear Regression

  • Lesson Introduction
  • The Setting
  • Exploring the data -- and why it matters
  • Visual Exploratory Analysis
  • A Non-Bayesian Linear Regression
  • A Simple PyMC model
  • Adding Predictors to our Model
  • Predicting Out-of-Sample
  • From Predictions to Business Insights
  • The Bayesian Workflow
  • Lesson Summary
  • Lesson References
  • Lesson Exercises
  • Lesson Feedback Form

Hierarchical Linear Regression

  • Lesson Introduction
  • Motivation for Hierarchical models
  • Distributions Over Parameters
  • Hierarchical Models
  • Effect of Hierarchy
  • Power of Bayes
  • Lesson References
  • Lesson Exercises
  • Lesson Feedback Form

The next steps in your Bayesian exploration

  • Continuing your journey after this cousre
  • Post Course Feedback
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