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

Josef Moudrik j.moudrik at gmail.com
Fri Nov 1 03:58:19 PDT 2013

I believe that the "markov-chain" corresponds to the random playouts -
each such playout is a chain of markov events - next position of board
depends on the current position, not on the path that led to this
position (maybe should regard number of captives along the
configuration of the stones as a current "position"). The goal of
course is to estimate the distribution of "win" probabilities for each
possible move, and choose the best.

On 1 November 2013 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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