
Markov Chain Monte Carlo for Bayesian Inference – The Metropolis Algorithm - shogunmike
https://www.quantstart.com/articles/Markov-Chain-Monte-Carlo-for-Bayesian-Inference-The-Metropolis-Algorithm
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Houshalter
I'm sad that every explanation of MCMC uses complicated symbolic explanations.
I was trying to explain it once and came up with a fairly intuitive way to
visualize it. Probably that is the way it was first discovered, though who
knows.

In words, you can visualize it as doing a random walk around the area of a
probability graph. Then it just turns into an explanation of why random walks
accurately sample from an area, and the various tricks used in MCMC methods to
make random walks more efficient.

Relatedly, bayes theorem makes much more sense when you visualize it, e.g.
this post: [https://oscarbonilla.com/2009/05/visualizing-bayes-
theorem/](https://oscarbonilla.com/2009/05/visualizing-bayes-theorem/)

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warrenmar
Not to hijack this post, but I'm saddened every time I see anything monte
carlo related referred to as the metropolis algorithm. When you're the boss,
what you say goes.

[http://andrewgelman.com/2014/06/30/invented-metropolis-
algor...](http://andrewgelman.com/2014/06/30/invented-metropolis-algorithm/)

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shogunmike
There is also a bit of historical discussion on Wikipedia about this:
[https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_al...](https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm#History)

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constantlm
This is so incredibly far over my head.

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douche
Yup, 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

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agumonkey
I came here to .. too late.

