A Markov chain is a very specific model type. If you don't model your problem as a Markov chain, mcmc is irrelevant.
Don't be fooled by the fact that it's "Monte Carlo" - anything from gradient descent to uniform sampling can use Monte Carlo methods. Or not! I recommend reading up on machine learning. Stanford has a very nice series of online lectures that comprise a healthy course of study. s. On Nov 1, 2013 12:45 PM, "Darren Cook" <dar...@dcook.org> wrote: > > 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: > > https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm > > Thanks Remi. > Quoting from that article: > "...[a] method for obtaining a sequence of random samples from a > probability distribution for which direct sampling is difficult. This > sequence can be used to approximate the distribution (i.e., to generate > a histogram)" > > This sounds exactly like using N monte-carlo simulations at a node in an > MCTS tree, generating a histograms of possible scores. The highest point > on the histogram is used as the best-guess estimate of the score. When > you have two peaks it implies some unstable situation, like a semeai. Etc. > > You mentioned the "purpose" is not the same. Can you elaborate? > > (If "Markov-Chain" is a nice clean synonym for "rules of the game", > whether the game is go or weather systems, I feel I am on home ground!) > > Darren > _______________________________________________ > Computer-go mailing list > Computer-go@dvandva.org > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go >
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