ST758 - Computing for Statistical Research
- Prerequisites: ST702, ST705
- Term & Frequency: Every fall
- Student Audience: PhD students in Statistics and related fields
- Credit: 3 credits
- Recent Texts: Numerical Methods of Statistics, second edition, by Monahan
- Recent Instructors: Eric Laber, Joe Guinness
- Background and Goals: This course is designed to prepare graduate students for numerical work in research in statistics and related areas. The computer language of instruction is R; some basic programming tools in R, including functions, will be covered. Homeworks will involve both analysis and computation in R; one will be a project involving a simulation experiment.
- Content: Computer arithmetic, basics of R, solving linear equations, Cholesky factorization, regression computations, eigenproblems, functions in R, optimization, numerical integration, random number generation, design and analysis of statistical simulation experiments.
- Alternatives: Numerical linear algebra covered by MA428; discrete event simulation offered in CSC/ISE/OR 762
- Subsequent Courses: None
S1 2017 Sections:
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