A visualization of the metropolis-hastings algorithm, a markov chain monte carlo method which utilizes dependent sampling for high-dimensional distributions.
The Metropolis-Hastings Algorithm is a memoryless algorithm where future outcomes are independent of the past. It allows us to compare probabilities of different outcomes and converge upon a stationary distribution.
For a high-level explanation, see this video from Harvard Online.