http://bjlkeng.github.io/enTue, 10 Mar 2026 20:54:59 GMTNikola (getnikola.com)http://blogs.law.harvard.edu/tech/rsshttp://bjlkeng.github.io/posts/markov-chain-monte-carlo-mcmc-and-the-metropolis-hastings-algorithm/Brian Keng<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> </div><div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>In this post, I'm going to continue on the same theme from the last post: <a href="http://bjlkeng.github.io/posts/sampling-from-a-normal-distribution/">random sampling</a>. We're going to look at two methods for sampling a distribution: rejection sampling and Markov Chain Monte Carlo Methods (MCMC) using the Metropolis Hastings algorithm. As usual, I'll be providing a mix of intuitive explanations, theory and some examples with code. Hopefully, this will help explain a relatively straight-forward topic that is frequently presented in a complex way.</p> <p><a href="http://bjlkeng.github.io/posts/markov-chain-monte-carlo-mcmc-and-the-metropolis-hastings-algorithm/">Read more…</a> (20 min remaining to read)</p></div></div></div>Markov ChainMCMCMetropolis-HastingsMonte Carloprobabilityrejection samplingsamplinghttp://bjlkeng.github.io/posts/markov-chain-monte-carlo-mcmc-and-the-metropolis-hastings-algorithm/Sun, 13 Dec 2015 20:05:56 GMT