On Mon, Mar 30, 2015 at 7:09 AM, Petr Baudis pa...@ucw.cz wrote:
On Mon, Mar 30, 2015 at 09:11:52AM -0400, Jason House wrote:
The complex formula at the end is for a lower confidence bound of a
Bernoulli distribution with independent trials (AKA biased coin flip) and
no prior knowledge. At
For Wilson, you can use depth to pick confidence bound.
s.
On Mar 30, 2015 7:09 AM, Petr Baudis pa...@ucw.cz wrote:
On Mon, Mar 30, 2015 at 09:11:52AM -0400, Jason House wrote:
The complex formula at the end is for a lower confidence bound of a
Bernoulli distribution with independent trials
The complex formula at the end is for a lower confidence bound of a
Bernoulli distribution with independent trials (AKA biased coin flip) and
no prior knowledge. At a leaf of your search tree, that is the most correct
distribution. Higher up in a search tree, I'm not so sure that's the
correct
On Mon, Mar 30, 2015 at 4:09 PM, Petr Baudis pa...@ucw.cz wrote:
The strongest programs often use RAVE or LGRF or something like that,
with or without the UCB for tree exploration.
Huh, are there any strong programs that got LGRF to work?
Erik
___
On Mon, Mar 30, 2015 at 09:11:52AM -0400, Jason House wrote:
The complex formula at the end is for a lower confidence bound of a
Bernoulli distribution with independent trials (AKA biased coin flip) and
no prior knowledge. At a leaf of your search tree, that is the most correct
distribution.
Hi,
When performing a montecarlo search, we end up with a number of wins
and number of looses for a position on the board.
What is now the proven methology for comparing these values?
I tried the method described here:
http://www.evanmiller.org/how-not-to-sort-by-average-rating.html
On Mon, Mar 30, 2015 at 04:17:13PM +0200, Erik van der Werf wrote:
On Mon, Mar 30, 2015 at 4:09 PM, Petr Baudis pa...@ucw.cz wrote:
The strongest programs often use RAVE or LGRF or something like that,
with or without the UCB for tree exploration.
Huh, are there any strong programs
