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140.724.01
Probability Theory IV

Location:
East Baltimore
Term:
4th term
Department:
Biostatistics
Credits:
3 credits
Academic Year:
2022 - 2023
Instruction Method:
In-person
Class Times:
  • Tu Th,  3:30 - 4:50pm
Auditors Allowed:
Yes, with instructor consent
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 in-person section of a course that is also offered virtually/online. 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.