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原文連結
論文資訊
- 類型：已發表論文
- 日期：2010
摘要
We show that the natural 縮放律 of measurement for a particular problem defines the most likely probability distribution of observations taken from that measurement scale. Our approach extends the method of maximum 熵 to use measurement scale as a type of 資訊 constraint. We argue that a very common measurement scale is linear at small magnitudes grading into logarithmic at large magnitudes, leading to observations that of ten follow Student's probability distribution which has a Gaussian shape for small fluctuations from the mean and a 冪次定律 shape for large fluctuations from the mean. An inverse 縮放律 of ten arises in which measures naturally grade from logarithmic to linear as one moves from small to large magnitudes, leading to observations that often follow a gamma probability distribution. A g
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