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Changing perspective is often a great way to solve burning research problems. Riemannian spaces are such a perspective change, as Arto Klami, an Associate Professor of computer science at the University of Helsinki and member of the Finnish Center for Artificial Intelligence, will tell us in this episode.
He explains the concept of Riemannian spaces, their application in inference algorithms, how they can help sampling Bayesian models, and their similarity with normalizing flows, that we discussed in episode 98.
Arto also introduces PreliZ, a tool for prior elicitation, and highlights its benefits in simplifying the process of setting priors, thus improving the accuracy of our models.
When Arto is not solving mathematical equations, you’ll find him cycling, or around a good board game.
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !
Thank you to my Patrons for making this episode possible!
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Takeaways:
- Riemannian spaces offer a way to improve computational efficiency and accuracy in Bayesian inference by considering the curvature of the posterior distribution.
- Riemannian spaces can be used in Laplace approximation and Markov chain Monte Carlo...
