Yes, I understood this.  The particular case I was trying to optimize used
two tunables, and it looked like there were two optimal combinations, but
CLOP chose a point between them that was less optimal.  I was checking the
interaction between the beta constant and the MFGO bias.

> -----Original Message-----
> From: computer-go-boun...@dvandva.org [mailto:computer-go-
> boun...@dvandva.org] On Behalf Of Rémi Coulom
> Sent: Tuesday, January 03, 2012 7:19 AM
> To: computer-go@dvandva.org
> Subject: [Computer-go] win rate bias and CLOP
> 
> It is important to understand that CLOP claims very little in terms of win
> rate. That is to say the win rate estimates it reports are all biased. Win
> rate over all samples underestimates the real win rate. Win rate near the
> maximum (central, and weighted) tend to be over-estimated.
> 
> CLOP finds the location in parameter space that has the highest win rate.
It
> may be the highest because it is the best, but also because it is the most
> lucky. That's why it is necessarily biased toward optimistic values.
> 
> If the win rate over all samples is an improvement, then you can be sure
you
> have an improvement. Otherwise you cannot be sure unless you actually play
a
> lot of games with the suggested parameters.
> 
> Rémi
> 
> On 3 janv. 2012, at 14:09, Ingo Althöfer wrote:
> 
> > Hi David,
> >
> > David Fotland on CLOP-optimization:
> >> I tried it, but got no benefit so far.  It claimed to find better
> settings
> >> for most parameters, but when I used them the program wasn’t any
> >> stronger.
> >
> > Interestant. Had it similar strength or did it even become weaker?
> > How often did the move proposals by your "older" ManyFaces and the
> > CLOP-MF differ?
> >
> > Ingo.
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