The central limit theorem predicts that a collection of objects

来源: marketreflections 于 2011-03-09 14:35:43 [博客] [旧帖] [给我悄悄话] 本文已被阅读：次

The central limit theorem predicts that a collection of objects in which there are no hidden

correlations (e.g. a handful of coins being tossed) will produce fluctuations which have an

approximate Gaussian distribution. By contrast, the fluctuations emerging from systems

containing correlations which cross multiple length and/or time scales can exhibit significant

deviations from Gaussian behavior.

The central limit theorem predicts that a collection of objects in which there are no hidden

correlations (e.g. a handful of coins being tossed) will produce fluctuations which have an

approximate Gaussian distribution. By contrast, the fluctuations emerging from systems

containing correlations which cross multiple length and/or time scales can exhibit significant

deviations from Gaussian behavior.

The central limit theorem predicts that a collection of objects in which there are no hidden

correlations (e.g. a handful of coins being tossed) will produce fluctuations which have an

approximate Gaussian distribution. By contrast, the fluctuations emerging from systems

containing correlations which cross multiple length and/or time scales can exhibit significant

deviations from Gaussian behavior.

The central limit theorem predicts that a collection of objects in which there are no hidden

correlations (e.g. a handful of coins being tossed) will produce fluctuations which have an

approximate Gaussian distribution. By contrast, the fluctuations emerging from systems

containing correlations which cross multiple length and/or time scales can exhibit significant

deviations from Gaussian behavior.