Week 3

Lecture

• Lecture #4: Bayesian vs Frequentist Inference
  • Lecture Video
  • Lecture Notes
  • Lecture Summary
• Lecture #5: Sampling for Posterior Simulation
  • Lecture Video
  • Lecture Notes
  • Lecture Summary

Activities

• In-Class Exercise #4: Bayesian versus Frequentist Inference
• In-Class Exercise #5: Introduction to Sampling
• In-Class Exercise #5: Solutions
• Homework #2
• Homework #2 Solutions

Reading

Applications and Broader Impact

1. Bayesian Modeling of Uncertainty in Ensembles of Climate Models
2. All together now: the most trustworthy covid-19 model is an ensemble

Bayesian versus Frequentist Inference

1. (Introductory) Comparison of Frequentist and Bayesian Inference
2. (Advanced) Properties of MLE: consistency, asymptotic normality, Fisher information
3. (Advanced) Consistency, asymptotic normality, and coverage

Sampling

1. (Introductory) Provable Randomness: How to Test RNGs
2. (Introductory) Basics of Sampling 
3. (More detailed) How to choose g and M in rejection sampling