Author: José Carlos
Date: 14:50:38 01/03/01
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On January 03, 2001 at 16:26:19, Robert Hyatt wrote: >On January 03, 2001 at 09:52:06, José Carlos wrote: > >> Lately, people have been talking here about significant results. I'm not >>really sure if probabilistic calculus is appropiate here, because chess games >>are not stocastic events. >> So, I suggest an experiment to mesure the probabilistic noise: >> >> -chose a random program and make it play itself. >> -write down the result after 10 games, 50 games, 100 games... >> >> It should tend to be an even result, and it would be possible to know how many >>games are needed to get a result with a certain degree of confidence. >> If we try this for several programs, and the results are similar, we can draw >>a conclusion, in comparison with pure probabilistic calculus. >> >> Does this idea make sense, or am I still sleeping? :) >> >> José C. > >It is statistically invalid. IE if you flip a coin 500 times do you _really_ >expect to get 250 heads and 250 tails? Probability distribution says you >won't get that very often at all. In fact, if you flip long enough, you will >either get 500 straight heads or tails, or else prove the coin is _not_ actually >perfectly random with 50-50 probability of getting a head or tail. But don't you think the more times you flip the coin, the closer the number of head and tails (in %) will be? Maybe the coin is not the better comparison, as it is a random event, and a chess game is not, but I still think it should work. But I expect a different rate of "closeness" (is this word correct?) for the same number of tries with the coin (random event) and the games (partially random -book, pondering, ... and partially not -eval function, search algos...), and that difference is what I want to measure. José C.
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