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Department: Biostatistics
Term: 2nd term
Credits: 3 credits
Contact: Roger Peng
Academic Year: 2012 - 2013
Course Instructor:

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 Objective(s):
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

Methods of Assessment: Method of student evaluation based on computing and theoretical assignments
Location: East Baltimore
Class Times:
  • Tuesday 8:30 - 9:50
  • Thursday 8:30 - 9:50
Enrollment Minimum: 10
Instructor Consent: No consent required

Prior programming experience; at least one year of doctoral-level statistics/biostatistics theory and methods courses; 140.776

Auditors Allowed: Yes, with instructor consent
Grading Restriction: Letter Grade or Pass/Fail