Randomization and Simulation Methods

Video playlist on YouTube

Units

1. Core concepts: In what ways is random sampling useful for data analysis?
   • Introduction
   • Why Monte Carlo methods?
   • Distributions and null models
   • Use cases
2. Sampling from data sets: foundations
   • The Jackknife
   • The Bootstrap
   • Randomization
3. Sampling from data sets: complex designs
   • Complex models
   • Beyond traditional models
   • Parallel processing in R
   • Parallel processing with Rscript
4. Sampling from data sets: decision errors and predicting new data
   • False positives
   • False negatives
   • Cross-validation
5. Simulating data for inference
   • Introduction to simulation modeling
   • Simulating null distributions
   • Approximate Bayesian computation
   • Genetic algorithm