MCMCteknikker
MCMCteknikker, or Markov Chain Monte Carlo techniques, are a class of computational algorithms that use random sampling to estimate properties of complex probability distributions. By constructing a Markov chain whose equilibrium distribution matches the target distribution, these methods generate dependent samples that can be used to approximate integrals, compute expectations, or sample from posterior distributions in Bayesian inference.
The foundational method, the Metropolis algorithm, proposes a new state from a symmetric proposal distribution and
More advanced methods such as Hamiltonian Monte Carlo (HMC) and its variants, including the No‑U‑Turn Sampler
MCMC techniques underpin many areas of science, including physics, economics, machine learning, and phylogenetics. Practical use