Efficient Bayesian inference with Hamiltonian Monte Carlo -- Michael Betancourt (Part 1)

Why HN readers recommended it

HN people return to it as a clear primer on MCMC and HMC and on why high dimensions behave counterintuitively.

This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.

Video & recommendation details

Creator / channel

MLSS Iceland 2014

Topic

hamiltonian monte carlo

Format

lecture

Level

advanced

HN recommenders in dataset

3

HN mentions in dataset

3

Recommendation years in dataset

2017–2024

These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.

From the original HN conversations

Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.

• Recently watched this on Hamiltonian Monte Carlo, and it was a fantastic primer on the basic concepts and motivations of mcmc, and later hmc [VIDEO]

• This is an interesting take and kudos to the author for relaying a helpful way to think about high dimensional distributions. I really like and often come back to this talk by Michael Betancourt where the theme is quite similar: [VIDEO]

• Not an expert in this field, but I'm guessing this is related to unintuitive nature of geometry in high-dimensional spaces. One rough example I can think of is the fact that the number of ways to move away from the origin increases exponentially (or even …