5 ms·
This is so incredibly far over my head.
by constantlm 11y ago
This is so incredibly far over my head.
- adenadel 11y agoThis could potentially help http://jeremykun.com/2015/04/06/markov-chain-monte-carlo-without-all-the-bullshit/ http://jeremykun.com/2015/04/06/markov-chain-monte-carlo-wit... If you're unfamiliar with Bayes Theorem and Bayesian inference the earlier articles from the OP might help.
- shogunmike 11y agoAlso, John Kruschke's "Doing Bayesian Data Analysis", aka the "Puppy Book", is a gentle introduction to Bayesian inference. It is very readable, especially with regards MCMC.
- douche 11y agoYup, going to have to save this for later... I vaguely remember a Metropolis Light Transport from my college graphics course, which I assume uses similar sampling methodology
- agumonkey 11y agoI came here to .. too late.
- doug1001 11y agoit's not. set aside for a moment the prose description, find an MH module in your language of choice and study the code then play around w/ it in the REPL. A couple of things will probably surprise you when you look at an MCMC implementation (either MH or Gibbs): (i) very compact (eg, MH ~ 40 loc in python; 120 in scala; Gibbs ~10 lines in python, ~40 in scala); and (ii) the primary data structures are ones you probably use every day (eg, 2D array, which is used to represent the transition probabilities among the nodes comprising the graph that models the random walk). (A python/numpy implementation of MH: http://isaacslavitt.com/2013/12/30/metropolis-hastings-and-slice-sampling/ http://isaacslavitt.com/2013/12/30/metropolis-hastings-and-s...)