importance sampling does: reweights samples from proposal to estimate target expectation

rejection sampling does: samples from target by accepting/rejecting proposals

Rejection sampling generates samples from a target distribution by accepting or rejecting proposed samples based on a proposal distribution

Resampling (statistics)

Bootstrapping samples with replacement to estimate distributions

Metropolis–Hastings algorithm

Metropolis-Hastings algorithm samples from difficult distributions

Top-k vs top-p sampling: top-k fixes candidate count, top-p fixes cumulative probability mass

Top-k sampling fixes candidate count; top-p sampling fixes cumulative probability mass

Maximum a posteriori estimation

MAP estimation incorporates a prior P(θ)

Langevin dynamics does: adds noise to gradient descent to sample from a distribution

Langevin dynamics adds noise to gradient descent to sample from a distribution