140.779.01
Advanced Statistical Computing
Location
East Baltimore
Term
4th Term
Department
Biostatistics
Credit(s)
3
Academic Year
2024 - 2025
Instruction Method
In-person
Tu, Th, 1:30 - 2:50pm
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
Every Year
Resources
Prerequisite
Prior programming experience; at least one year of doctoral-level statistics/biostatistics theory and methods courses; 140.776
Covers the theory and application of common algorithms used in statistical computing. Includes topics: root finding, optimization, numerical integration, Monte Carlo, Markov chain Monte Carlo, stochastic optimization, and bootstrapping. Discusses specific algorithms: Newton-Raphson, EM, Metropolis-Hastings algorithm, Gibbs sampling, simulated annealing, Gaussian quadrature, Romberg integration, etc. Discusses applications of these algorithms to real research problems.
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
- Understand mathematical properties of common statistical algorithms
- Implement statistical algorithms using a high-level statistical programming language
Methods of Assessment
This course is evaluated as follows:
- 50% Computing
- 50% Theoretical assignments