The Future of Faster MCMC

<p>Today's clip is from Episode 163, featuring Eliot Carlson and Adrian Seyboldt. In this conversation, Eliot and Adrian look beyond current approaches to HMC adaptation and preconditioning and share the ideas they're most excited to explore next.<br /><br />Eliot discusses new ways of parallelizing MCMC by solving for an entire trajectory at once rather than computing every step sequentially, a potentially powerful direction for expensive, high-dimensional problems. Adrian, meanwhile, talks about exploring non-adjusting methods and going beyond first-order information by investigating how higher-order autodiff and second-order derivatives could open up new possibilities for sampling.<br /><br />It's a glimpse into some of the ideas that could help make MCMC faster and more scalable as computational hardware continues to become increasingly parallel.<br /></p><p>Full discussion <a rel="noopener noreferrer nofollow" href="https://learnbayesstats.com/episode/faster-sampling-mass-matrix-adaptation-hmc-nutpie-seyboldt-carlson" target="_blank">here</a></p><p></p><p>Support &amp; Resources<br />→ Support the show on <a rel="noopener noreferrer nofollow" href="https://www.patreon.com/c/learnbayesstats" target="_blank">Patreon</a><br />→ <a rel="noopener noreferrer nofollow" href="https://topmate.io/alex_andorra/1011122" target="_blank">Bayesian Modeling Course</a> (first 2 lessons free): <br />Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome <a rel="noopener noreferrer nofollow" href="https://bababrinkman.com/" target="_blank">work!</a></p>