Instructor
Textbook
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Rizzo (2019). Statistical Computing with R, 2nd Edition, CRC
Reference
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Givens and Hoeting (2012). Computational Statistics, Wiley
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Matloff (2011). The Art of R Programming: A Tour of Statistical Software Design, No Starch Press
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Wickham, Çetinkaya-Rundel, and Grolemund. (2023) R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 2nd Edition, O'Reilly Media
Schedule
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Week 1: Introduction to R. Probability and Statistics Review.
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Week 2: Methods for Generating Random Variables.
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Week 3: Methods for Generating Random Vectors.
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Week 4: Monte Carlo Integration. Antithetic Variables.
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Week 5: Control Variates. Importance Sampling.
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Week 6: Stratified Sampling.
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Week 7: Monte Carlo Methods in Estimation.
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Week 8: Monte Carlo Methods in Hypothesis Testing.
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Week 9: Midterm.
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Week 10: The Bootstrap. Bootstrap Confidence Intervals.
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Week 11: The Jackknife. Cross Validation.
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Week 12: Introduction to Bayesian Analysis. Markov Chain Monte Carlo Methods.
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Week 13: The Metropolis-Hastings Algorithm.
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Week 14: The Gibbs Sampler. Monitoring Convergence.
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Week 15: EM Algorithm.
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Week 16: Variational Inference.
Lecture Video