1. Bernd Berg's MCMC Class at FSU (link). The lectures are clear and concise, with a bias towards statistical physics.
2. Michael Mascagni's Monte Carlo class at FSU. The landing page has many useful links, and the lecture notes are available here. The class focuses on random number generation, direct Monte Carlo methods, and using MC to solve PDEs.
3. MCMC for computer vision at UCLA. Includes some advanced topics like reversible-jump MCMC.
4. Glenn Cowan at University of London has a great course (and accompanying text) on Statistical Data Analysis, which has a fair amount of overlap with some MC topics.
5. A very nice iPython/jupyter notebook powered MCMC class at Harvard.
6. Werner Krauth has an excellent Coursera class on Statistical Mechanics with lots of MC and MCMC computation.
7. Of course, my own course notes are here.
2. Michael Mascagni's Monte Carlo class at FSU. The landing page has many useful links, and the lecture notes are available here. The class focuses on random number generation, direct Monte Carlo methods, and using MC to solve PDEs.
3. MCMC for computer vision at UCLA. Includes some advanced topics like reversible-jump MCMC.
4. Glenn Cowan at University of London has a great course (and accompanying text) on Statistical Data Analysis, which has a fair amount of overlap with some MC topics.
5. A very nice iPython/jupyter notebook powered MCMC class at Harvard.
6. Werner Krauth has an excellent Coursera class on Statistical Mechanics with lots of MC and MCMC computation.
7. Of course, my own course notes are here.
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