MATH 396 A: Finite Markov Chains and Monte-Carlo Methods

Spring 2021
Meeting:
MWF 10:30am - 11:20am / * *
SLN:
16734
Section Type:
Lecture
Joint Sections:
STAT 396 A
Instructor:
SEE CATALOG FOR UPDATED COURSE DESC RIPTION AND PREREQS OFFERED VIA REMOTE LEARNING PERIOD III MAJOR RESTRICTIONS WILL BE REVIEWED MARCH 31 ~9AM. PLEASE CHECK BACK ON THIS DAY/TIME TO SEE IF NON-MAJORS CAN REGISTER.
Catalog Description:
Finite Markov chains; stationary distributions; time reversals; classification of states; classical Markov chains; convergence in total variation distance and L2; spectral analysis; relaxation time; Monte Carlo techniques: rejection sampling, Metropolis-Hastings, Gibbs sampler, Glauber dynamics, hill climb and simulated annealing; harmonic functions and martingales for Markov chains. Prerequisite: a minimum grade of 2.0 in MATH 208; a minimum grade of 2.0 in either MATH 394/STAT 394, CSE 312, or STAT 340; and a minimum grade of 2.0 in either STAT 395/MATH 395, STAT 341, or STAT 391. Offered: jointly with STAT 396; Sp.
GE Requirements Met:
Natural Sciences (NSc)
Credits:
3.0
Status:
Active
Last updated:
February 6, 2025 - 10:53 am