Metropolis-Hastings

Model Perspective

Nov 29, 2022 · Artificial Intelligence

MCMC Demystified: Monte Carlo Basics, Metropolis-Hastings & Gibbs Sampling

Markov Chain Monte Carlo (MCMC) extends classic Monte Carlo by generating dependent samples via a Markov chain, enabling Bayesian inference through concepts like the plug‑in principle, burn‑in, asymptotic independence, and algorithms such as Metropolis‑Hastings and Gibbs sampling, while addressing convergence and effective sample size.

Bayesian inferenceGibbs samplingMCMC

0 likes · 13 min read

MCMC Demystified: Monte Carlo Basics, Metropolis-Hastings & Gibbs Sampling

Model Perspective

Oct 22, 2022 · Fundamentals

Unlocking Bayesian Sampling: How MCMC and Hamiltonian Monte Carlo Work

This article explains the principles behind Markov Chain Monte Carlo methods, including Monte Carlo sampling, the Metropolis‑Hastings algorithm, and the Hamiltonian Monte Carlo (HMC) approach, illustrating how they efficiently approximate posterior distributions in Bayesian analysis.

Bayesian inferenceHamiltonian Monte CarloMCMC

0 likes · 11 min read

Unlocking Bayesian Sampling: How MCMC and Hamiltonian Monte Carlo Work

Model Perspective

Oct 4, 2022 · Artificial Intelligence

How Metropolis-Hastings Improves MCMC Sampling Efficiency

This article explains the detailed‑balance condition for Markov chains, shows why finding a transition matrix for a given stationary distribution is hard, and demonstrates how Metropolis‑Hastings modifies MCMC to achieve higher acceptance rates with a concrete Python example.

MCMCMarkov ChainMetropolis-Hastings

0 likes · 9 min read

How Metropolis-Hastings Improves MCMC Sampling Efficiency