metropolis-hastings

The point of Metropolis-Hastings is to sample from a distribution when you do not know the partition function.

Other surnames were commonly mangled in weird ways - Nicholas Metropolis (of the Metropolis-Hastings algorithm) surname was Μητρόπουλος (Mitropoulos).

Metropolis-Hastings algorithm works better for situations for which you know even less about what a good borgizaf looks like.

There's a reason that the Metropolis-Hastings Algorithm was such a big breakthrough, and that's because sampling is often quite difficult.

Took me back to my path tracing days, with things like importance sampling[1] and Metropolis-Hastings[2].

I always completely forget that the metropolis-hastings algorithm is named after someone whose last name is actually Metropolis.

The best example is probably the Metropolis-Hastings algorithm which was invented by nuke people.

This uses Goodman-Weare instead of Metropolis-Hastings, which works a lot better in practice, without having to figure out proposal distributions and so on.

This is achieved through a set of quantum registers that track state evolution, transition probabilities, and acceptance criteria using a modified Metropolis-Hastings rule.

Like many things in machine learning, this is a simplified version of Metropolis-Hastings.

Stuff like Gibbs samplers and Metropolis-Hastings tends to run into issues once you get beyond ~100 parameters, sometimes more with clever graph structure.

Metropolis-Hastings algorithm, but I don't currently get it (the translation to the probability domain escapes me).

Lastly, much of the post is in fact a plain-language explanation of the Metropolis-Hastings algorithm, and he adds: "...

You’re also welcome to say “Metropolis-Hastings algorithm” (assuming that’s the specific algorithm you’re using).

"The point of Metropolis-Hastings is to sample from a distribution when you do not know the partition function." That's one point, yes.