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.
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
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