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On Sep 24, 2008, at 5:16 PM, Jason House [EMAIL PROTECTED]
wrote:
On Sep 24, 2008, at 2:40 PM, Jacques Basaldúa [EMAIL PROTECTED] wr
ote:
Therefore, the variance of the normal that best approximates the
distribution of both RAVE and
wins/(wins + losses) is the
On Fri, Sep 26, 2008 at 9:29 AM, Jason House
[EMAIL PROTECTED] wrote:
Sent from my iPhone
On Sep 24, 2008, at 5:16 PM, Jason House [EMAIL PROTECTED]
wrote:
On Sep 24, 2008, at 2:40 PM, Jacques Basaldúa [EMAIL PROTECTED] wrote:
Therefore, the variance of the normal that best approximates
Sent from my iPhone
On Sep 27, 2008, at 10:14 AM, Álvaro Begué [EMAIL PROTECTED]
wrote:
On Fri, Sep 26, 2008 at 9:29 AM, Jason House
[EMAIL PROTECTED] wrote:
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On Sep 24, 2008, at 5:16 PM, Jason House
[EMAIL PROTECTED]
wrote:
On Sep 24, 2008, at 2:40 PM, Jacques
The approach of this paper is to treat all win rate estimations as
independent estimators with
additive white Gaussian noise.
Have you tried if that works? (As Łukasz Lew wrote experimental setup
would be useful) I guess
there may be a flaw in your idea, but I am not a specialist. I will try
