> 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
