Syllabus
Homework One
Homework Two
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Slides:
August 27: Lesson One: Random Number Generators
August 29: Lesson Two: Testing RNGs, Independence
August 31: Lesson Three: Testing RNGs, Independence (cont.)
September 5: Lesson Four: Testing RNGs, Uniformity
September 7: Lesson Five: Generating Discrete Random Variables
September 7-10: Lesson Six: Generating (Some) Continuous Random Variables
September 10: Lesson Seven: Monte Carlo Integration
September 12th: Freestyle!
September 14: Lesson Eight: More Monte Carlo Integration and Variance Reduction Techniques
September 17: Lesson Nine: Summary of Specific RVs Simulation
September 17: Lesson Ten: Poisson Process Modeling
September 24: Lesson Ten b: More Poisson Process
September 24: Lesson Eleven: Non-Homogeneous Poisson Process
September 28: Lesson Twelve: The Spatial Poisson Process
October 1: Lesson Thirteen: Gambler's Ruin and Differential Equations
October 3-5: Lesson Fourteen: Discrete Time Markov Chains
October 8: No class
October 10: Lesson Fifteen: Continuous Time Markov Chains
October 12: Lesson Sixteen: A Brief Intro to Queueing
October 12: Lesson Seventeen: Markov Chain Monte Carlo
October 17: Lesson Eighteen: More MCMC
October 22: Lesson Nineteen: Horrible Slides for Aux. Vars.
October 26: Lesson Twenty: Some Convergence Topics.
October 31: Lesson Twenty One: Heavy Tails
November 5: Lesson Twenty Two: Bayesian Networks and Gelman and Rubin's R,
Minus the Stuff About Gelman and Rubin's R
November 9: Lesson Twenty Three: A Horrible Set of Perfect Simulation SlidesThat I'm Not Going to Really Follow Anyway
November 16: Lesson Twenty Four: Perfect IMH
November 26: Lesson Twenty Five: Feynman Diagram Resummation
November 28: Lesson Twenty Six: Stochastically Dominating Upper Processes
Extra Stuff:
Chi-square table
Kolmogorov-Smirnov CVs
Box-Muller Transformation
Polar-Marsaglia Transformation
Poisson from o(h)
