ADVANCED STATISTICAL COMPUTING Syllabus
Course Learning Objectives
Upon successfully completing this course, students will be able to:
- describe common deterministic statistical algorithms, such as root finding, numerical integration methods, Newton-Raphson, quasi-Newton methods, EM
- describe common stochastic algorithms used in statistics, such as Monte Carlo methods, Markov Chain Monte Carlo, stochastic optimization, Gibbs sampling, Metropolis-Hastings method
- Discuss mathematical properties of common statistical algorithms
- implement statistical algorithms using a high-level statistical programming language
Course DescriptionCovers the theory and application of common algorithms used in statistical computing. Topics include root finding, optimization, numerical integration, Monte Carlo, Markov chain Monte Carlo, stochastic optimization and bootstrapping. Some specific algorithms discussed include: Newton-Raphson, EM, Metropolis-Hastings algorithm, Gibbs sampling, simulated annealing, Gaussian quadrature, Romberg integration, etc. Also discusses applications of these algorithms to real research problems.
Intended AudienceThis course is primarily intended for Biostatistics doctoral students past their first year of study.
Methods of AssessmentMethod of student evaluation based on computing and theoretical assignments
PrerequisitesPrior programming experience; at least one year of doctoral-level statistics/biostatistics theory and methods courses; 140.776
Additional Faculty Notes:
The first four weeks will cover some of the topics in Numerical Optimization by Jorge Nocedal and Stephen Wright. You can download this book for free from the Springer website:
And the first edition also appears to be available in full form here:
Please see the course Session for a full list of dates and items for this course.
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