[Computer-go] Monte-carlo simulations vs. MCMC

Rémi Coulom Remi.Coulom at free.fr
Fri Nov 1 03:59:56 PDT 2013

MCMC has little to do with what we do in computer Go. In MCTS we have a Markov Chain and we take Monte-Carlo samples from it, but the purpose is really not the same at all as what MCMC algorithms do. I recommend the wikipedia articles. It is difficult to really get an idea of MCMC by reading a general description. It is probably best that you get a feeling of what it is by studying the details of a real MCMC algorithm. The most basic MCMC algorithm is the Metropolis-Hastings algorithm:


On 1 nov. 2013, at 02:49, Darren Cook <darren at dcook.org> wrote:

> I was reading this post "How would you explain Markov Chain Monte Carlo
> (MCMC) to a layperson?":
>  http://stats.stackexchange.com/q/165/5503
> The first few answers confused me, definitely not layperson-ready I
> thought! But then these two talked about it in the context of board
> games, so I kind of got the idea:
>  http://stats.stackexchange.com/a/438/5503
>  http://stats.stackexchange.com/a/12680/5503
> But this just sounded like Monte-Carlo simulations. Which bit is the
> "Markov-Chain"? I thought I'd post here in the hope that someone could
> explain Markov-Chain Monte Carlo in computer go terms. Are we already
> using Markov-Chains in MCTS, just by another name? If not, why not?
> (I.e. is it an idea that was tried but didn't work very well for reasons
> we don't understand very well? Or there something about the nature of
> the go rules that mean it cannot be done? etc.)
> Thanks,
> Darren
> -- 
> Darren Cook, Software Researcher/Developer
> http://dcook.org/work/ (About me and my work)
> http://dcook.org/blogs.html (My blogs and articles)
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