140.724.41
Probability Theory IV

Location
Internet
Term
4th Term
Department
Biostatistics
Credit(s)
3
Academic Year
2023 - 2024
Instruction Method
Synchronous Online
Class Time(s)
M, W, 1:30 - 2:50pm
Auditors Allowed
No
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Ciprian Crainiceanu
Contact Name
Ciprian Crainiceanu
Frequency Schedule
Every Year
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. Examine and apply foundational concepts of probability theory
  2. Assess the convergence of a sequence or series of random variables using martingale theory
  3. Classify the states and derive the transition probabilities of a Markov chain
  4. Derive the stationary distribution of certain Markov chains
  5. Identify 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
Consent Contact
ccraini1@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.