Computational Statistics: Monte Carlo and Optimization
STAT 221
Subject & Catalog Number
Course Information
Description
Computational tools for statistical inference and learning, with emphasis on the computational aspects of statistics, the statistical implications of inference algorithms, and the mathematical foundations for these ideas. Topics include: optimization methods such as Newton-Raphson and gradient-based algorithms; the EM algorithm; variational approximations; Monte Carlo methods, including Markov chain Monte Carlo, importance sampling, data augmentation, and sequential Monte Carlo; generative ML methods including diffusions and normalizing flows.
Course Notes
Computer programming exercises will apply the methods discussed in class.
School
Faculty of Arts & Sciences
Credits
4
Cross Reg
Available for Harvard Cross Registration
Department
Statistics
Course Component
Lecture
Subject
Statistics
Grading Basis
FAS Letter Graded
Course Requirements
Pre-Requisite: (STAT 110 or STAT 210) AND (STAT 171 or STAT 212) (STAT 212 may be taken concurrently).
General Education
N/A
Quantitative Reasoning with Data
N/A
Divisional Distribution
Science & Engineering & Applied Science
Course Level
Primarily for Graduate Students
