On Wed, Dec 03, 2003 at 12:11:39PM -0800, Martin Stone Davis wrote:
> Toad wrote:
> 
> >On Wed, Dec 03, 2003 at 11:34:06AM -0800, Martin Stone Davis wrote:
> >
> >>>Interesting. But I'm not sure whether your function for DDF is
> >>>the best one. Further I'm not sure whether the problem at hand
> >>>(average a bernoulli distribution) has a good solution without
> >>>memorizing the last N values.
> >>
> >>Well, IF the variable truly acts like a random Bernoulli variable, then 
> >>I doubt it's necessary to memorize the last N values.
> >>
> >>However, if it does something like this (for example): 1 1 0 0 0 0 1 1 0 
> >>0 1 1 1 1 0 0, where every other value really depends on the prior 
> >>value, then some kind of pattern-recognition algorithm could figure that 
> >>out and allow us to make better predictions.  We human beings should 
> >>look at the pattern of failures/successes for each binary variable we 
> >>use in the estimator to see that my assumption of a random Bernoulli is 
> >>a good one.
> >
> >
> >It would be useful to have that data on the RT node detail page.
> 
> Agreed, but note the following: If the DDRA is close to 0 or 1, then we 
> need to show more values than if the DDRA is close to 0.5.  Otherwise, 
> we won't have a good idea about what is happening with thvariable.

Why not just have a large fixed number? You'll have to have a limit
anyway.
> 
> We should shoot for DDRA*(1-DDRA)*nValuesToShow>=5, so let
> 
> nValuesToShow=MIN(5/(DDRA*(1-DDRA)),MAXIMUM_YOU_CAN_STAND_TO_SHOW_ON_DETAIL_PAGE)
> 
> -Martin
-- 
Matthew J Toseland - [EMAIL PROTECTED]
Freenet Project Official Codemonkey - http://freenetproject.org/
ICTHUS - Nothing is impossible. Our Boss says so.

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