STAT 396

Finite Markov Chains and Monte-Carlo Methods
3.0 credits

Course 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; and either a minimum grade of 2.0 in MATH 394/STAT 394 and STAT 395/MATH 395, or a minimum grade of 2.0 in STAT 340 and STAT 341, or a minimum grade of 2.0 in STAT 340 and STAT 395/MATH 395. Offered: jointly with MATH 396; Sp.

Syllabus Downloads

Upcoming Time Schedule

Spring, 2023

SLN/Section Time Location Instructor
20163 A
Open

M W F 9:30AM - 10:20AM

CMU 120

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