Dive into a comprehensive lecture on sampling techniques presented by Andreas Eberle from the University of Bonn at the Geometric Methods in Optimization and Sampling Boot Camp. Explore various sampling methods, including direct methods, acceptance-rejection techniques, and Markov chain Monte Carlo. Gain insights into creating samples from probability distributions, analyzing running times, and understanding mixing and relaxation times. Examine probabilistic and analytic approaches, independent samplers, proposal kernels, and the Random Walk Metropolis algorithm. Investigate concepts such as conductance and detailed balance, essential for mastering advanced sampling techniques in optimization and computational statistics.
Overview
Syllabus
References
Direct Methods
Acceptance rejection
Creating samples from mu
Running time
Markov chain Monte Carlo
Mixing time
Relaxation times
probabilistic approaches
analytic approaches
Independent sampler
Proposal kernel
Random work metropolis
Conductance
Detailed Balance
Taught by
Simons Institute
