
A Zero-Math Introduction to Markov Chain Monte Carlo Methods - tosh
https://towardsdatascience.com/a-zero-math-introduction-to-markov-chain-monte-carlo-methods-dcba889e0c50
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kgwgk
Previous discussion:
[https://news.ycombinator.com/item?id=15986687](https://news.ycombinator.com/item?id=15986687)

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donmatito
Random-play Monte-Carlo was the first algorithm that lead to good computer Go
software, before neural network. It was around 2008 I think. Before that,
pattern-base algos were really, really bad (like, barely above human beginner
level).

I'm not a mathematician, but the paper itself was a real beauty. I remember
vividly the parameter that balanced "exploitation" of apparently-good paths,
and "exploration" of unknown/apparently-bad path. I used it in many analogies
discussing innovation programs within large companies.

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hackandtrip
Is this [1] the paper you are referring to? Thanks for the heads up, this work
looks interesting.

[1]:
[https://www.aaai.org/Papers/AIIDE/2008/AIIDE08-036.pdf](https://www.aaai.org/Papers/AIIDE/2008/AIIDE08-036.pdf)

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hackandtrip
Are there any Lot-Of-Math Introduction to Monte Carlo Methods?

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theoh
I'm not sure, since it doesn't introduce the notion of detailed balance,
whether this article really deals meaningfully with the use of Markov chains
at all.

It doesn't bring out the fact that the Markov chain transition probabilities
have to be tuned to explore the parameter space. The relative efficiency of
MCMC versus a naive random sampling approach depends on this leveraging of
detailed balance so that the correlations of the Markov chain work in favour
of the experiment.

So given that the article introduces this notion of a random walk, so it seems
like it's going to discuss the Metropolis algorithm, it's not great that it
ducks the main issue which is why a correlated Markov chain random walk is a
useful approach.

The key is that it's a "conditioned" random walk, and the method by which it
is conditioned is the real trick to MCMC (at least to Metropolis, which is the
cool kind.)

