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140.724.41
Probability Theory IV

Location:
East Baltimore
Term:
4th term
Department:
Biostatistics
Credits:
3 credits
Academic Year:
2022 - 2023
Instruction Method:
Hybrid In-person and Synchronous Online
Class Times:
  • Tu Th,  3:30 - 4:50pm
Auditors Allowed:
No
Undergrads Allowed:
No
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Contact:
Abhirup Datta
Resources:
Prerequisite:

Calculus, real analysis; 140.721-3

Description:

Covers basic stochastic processes including martingales and Markov chains, followed by consideration of Markov Chain Monte Carlo (MCMC) methods.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Assess the convergence of a sequence or series of random variables using martingale theory
  2. Classify the states and derive the transition probabilities of a Markov chain
  3. Derive the stationary distribution of certain Markov chains
  4. Understand the principles of MCMC algorithms like Gibbs sampler and Metropolis-Hastings
Methods of Assessment:

This course is evaluated as follows:

  • 30% Homework
  • 30% In-class midterm exam
  • 40% In-class final exam

Instructor Consent:

Consent required for some students

Consent Note:

Consent required for students who are not in the Biostatistics PhD program

For consent, contact:

abhidatta@jhu.edu

Special Comments:

Please note: This is the virtual/online section of a course that is also offered onsite. Students will need to commit to the modality for which they register. The course may include one or two lab sessions scheduled for the corresponding lecture periods.