My implementation is very basic (and inefficient). I use Gibbs sampling (ie, Metropolis-Hastings, one dimension at a time, which scales better to higher dimensions), with uniform samples over the parameter range. Details of the implementation are in CSPWeight.cpp. I found it is good enough in practice, but I will improve it. It should be easy to use the quadratic regression to define a candidate distribution that is much better than uniform.
http://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm http://en.wikipedia.org/wiki/Gibbs_sampling Also, an unrelated note about priors: it is a good idea to use a pessimistic prior for the mean/lcb estimation, and a more optimistic prior for the regression. I did not mention it in the paper. It prevents the algorithm from iterating forever until the winning rate is close to 100%. It is not extremely critical for performance, but it may help a bit. Rémi On 15 nov. 2011, at 17:59, Brian Sheppard wrote: > I would like to know more about the exploration methods that you tested in > CLOP. Let's start with Metropolis-Hastings. > > I understand Metropolis-Hastings as having a current point P, which has a > weight Wp, and randomly sampling a point Q, which has weight Wq. Then your > next point will be Q if Wq >= Wp, or if Wq < Wp then move to Q with > probability Wq/Wp, and keep P otherwise. Do I have that right? > > My question concerns the space over which Q is sampled. Is it just random > over the whole domain? Or a radius around P? > > Thanks, > Brian > > > _______________________________________________ > Computer-go mailing list > [email protected] > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go _______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
