140.724.01
Probability Theory IV
 Location:
 East Baltimore
 Term:
 4th term
 Department:
 Biostatistics
 Credits:
 3 credits
 Academic Year:
 2022  2023
 Instruction Method:
 Inperson
 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; 140.7213
 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:
 Identify foundational concepts of probability theory
 Assess the convergence of a sequence or series of random variables using martingale theory
 Classify the states and derive the transition probabilities of a Markov chain
 Derive the stationary distribution of certain Markov chains
 Identify the principles of MCMC algorithms like Gibbs sampler and MetropolisHastings
 Methods of Assessment:
This course is evaluated as follows:
 30% Homework
 30% Inclass midterm exam
 40% Inclass 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:
 Special Comments:
Please note: This is the inperson 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.