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
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
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
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
On 1 nov. 2013, at 13:32, 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
2013/11/1 Rémi Coulom remi.cou...@free.fr
In MCMC the distribution is given to you with some kind of mathematical
definition, and the challenge is to create a Markov Chain that approximates
the distribution well.
In MCTS what we really want is a good playout policy and we sample (do
Look up graphical models in the context of machine learning.
s.
On Nov 1, 2013 3:49 AM, Darren Cook dar...@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
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
