140.724.01
Probability Theory IV

Location
East Baltimore
Note: Due to the COVID-19 Pandemic, this course was held in a virtual/online format.
Term
4th Term
Department
Biostatistics
Credit(s)
3
Academic Year
2020 - 2021
Instruction Method
TBD
Class Time(s)
Tu, Th, 3:30 - 4:50pm
Auditors Allowed
No
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Abhirup Datta
Contact Name
Abhirup Datta
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. 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
Instructor Consent
Consent required for some students
Consent Note
Consent required for students who are not in the Biostatistics PhD program
Consent Contact
abhidatta@jhu.edu
Special Comments

The course may include one or two lab sessions scheduled for the corresponding lecture periods.