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.

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.

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.


Full discussion here [https://learnbayesstats.com/episode/faster-sampling-mass-matrix-adaptation-hmc-nutpie-seyboldt-carlson]

Support & Resources
→ Support the show on Patreon [https://www.patreon.com/c/learnbayesstats]
→ Bayesian Modeling Course [https://topmate.io/alex_andorra/1011122] (first 2 lessons free):
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work! [https://bababrinkman.com/]

The Future of Faster MCMC

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

Are you a researcher or data scientist / analyst / ninja? Do you want to learn
Bayesian inference, stay up to date or simply want to understand what Bayesian
inference is? Then this podcast is for you! You'll hear from researchers and
practitioners of all fields about how they use Bayesian statistics, and how in
turn YOU can apply these methods in your modeling workflow. When I started
learning Bayesian methods, I really wished there were a podcast out there that
could introduce me to the methods, the projects and the people who make all that
possible. So I created "Learning Bayesian Statistics", where you'll get to hear
how Bayesian statistics are used to detect black matter in outer space, forecast
elections or understand how diseases spread and can ultimately be stopped. But
this show is not only about successes -- it's also about failures, because
that's how we learn best. So you'll often hear the guests talking about what
*didn't* work in their projects, why, and how they overcame these challenges.
Because, in the end, we're all lifelong learners! My name is Alex Andorra by the
way, and I live in Paris. By day, I'm a data scientist and modeler at the
https://www.pymc-labs.io/ (PyMC Labs) consultancy. By night, I don't (yet) fight
crime, but I'm an open-source enthusiast and core contributor to the python
packages https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/
(ArviZ). I also love https://www.pollsposition.com/ (election forecasting) and,
most importantly, Nutella. But I don't like talking about it – I prefer eating
it. So, whether you want to learn Bayesian statistics or hear about the latest
libraries, books and applications, this podcast is for you -- just subscribe!
You can also support the show and https://www.patreon.com/learnbayesstats
(unlock exclusive Bayesian swag on Patreon)! This podcast uses the following
third-party services for analysis: Podcorn - https://podcorn.com/privacy

The Future of Faster MCMC

Recommended