Seminar by Justin Ko
Probability seminar seriesJustin Ko Room: M3 3127 |
Probability seminar seriesJustin Ko Room: M3 3127 |
Statistics and Biostatistics seminar seriesJemila Hamid Room: M3 3127 |
Actuarial Science and Financial Mathematics seminar seriesGee Lee Room: M3 3127 |
Statistics and Biostatistics seminar seriesJiahua Chen Room: M3 3127 |
Probability seminar seriesJonathan Husson Room: M3 3127 |
Statistics and Biostatistics seminar seriesMin-ge Xie Room: M3 3127 |
Waterloo Student Conference in Statistics, Actuarial Science and Finance
Distinguished Lecture Series Jeffrey Rosenthal Room: DC 1351 |
Speeding up Metropolis using Theorems
Markov chain Monte Carlo (MCMC) algorithms, such as the Metropolis algorithm, are designed to converge to complicated high-dimensional target distributions, to facilitate sampling. The speed of this convergence is essential for practical use. In this talk, we will present several theoretical results which can help improve the Metropolis algorithm's convergence speed. Specific topics will include: diffusion limits, optimal scaling, optimal proposal shape, tempering, adaptive MCMC, the Containment property, and the notion of adversarial Markov chains. The ideas will be illustrated using the simple graphical example available at probability.ca/met. No particular background knowledge will be assumed.
Statistics and Biostatistics seminar seriesAaron Schein Room: M3 3127 |