Skip to main content

140.778.01
Advanced Statistical Computing

Location
East Baltimore
Term
2nd Term
Department
Biostatistics
Credit(s)
3
Academic Year
2012 - 2013
Instruction Method
TBD
Class Time(s)
Tu, Th, 8:30 - 9:50am
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
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. 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.
Learning Objectives
Upon successfully completing this course, students will be able to:
  1. describe common deterministic statistical algorithms, such as root finding, numerical integration methods, Newton-Raphson, quasi-Newton methods, EM
  2. describe common stochastic algorithms used in statistics, such as Monte Carlo methods, Markov Chain Monte Carlo, stochastic optimization, Gibbs sampling, Metropolis-Hastings method
  3. Discuss mathematical properties of common statistical algorithms
  4. implement statistical algorithms using a high-level statistical programming language