140.779.01
Advanced Statistical Computing
- Location:
- East Baltimore
- Term:
- 4th term
- Department:
- Biostatistics
- Credits:
- 3 credits
- Academic Year:
- 2022 - 2023
- Instruction Method:
- In-person
- Class Times:
-
- M W, 9:00 - 10:20am
- Auditors Allowed:
- Yes, with instructor consent
- Undergrads Allowed:
- No
- Grading Restriction:
- Letter Grade or Pass/Fail
- Course Instructor:
- Contact:
- Kasper Hansen
- Frequency Schedule:
- Every Other Year
- Next Offered:
- 2024 - 2025
- Resources:
- Prerequisite:
Prior programming experience; at least one year of doctoral-level statistics/biostatistics theory and methods courses; 140.776
- Description:
-
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
- Instructor Consent:
No consent required