Hey, I've read this article while doing my course exercises. Coming from Metropolis-Hastings, HMC seemed a bit like magic. Here's some notes that I wrote about

Hey, I've read this article while doing my course exercises. Coming from Metropolis-Hastings, HMC seemed a bit like magic. Here's some notes that I wrote about it, could somebody tell was I even remotely close: >In other words explained by my TA: when you have a really complex multi-dimensional distribution normal MCMC will take forever to explore it. HMC on the other hand adds momentum that will help the MC to explore areas where the probability is high. Imagine the probability is being translated into 3d landscape where high probability corresponds to deep areas and low high. Any ball with gravity will follow those curvatures and not jump over the walls needlessly where the probability is low. >Also HMC is the current state-of-the-art MCMC algorithm if you have very high-dimensional data. Regular MCMC too can be applied if the distribution is much simpler. However instead of MCMC, VI is commonly used since it can give really good results with little amount of work. I mean sure you have to choose your approximation distribution but after that it's dead simple. Only maybe if you need really high accuracy you might choose something like HMC.