测量信息的计算

测量信息反映了测量者、测量仪器、先验知识等因数对测量效果的影响，可以用测量过程所减少的不确定性来量化。测量信息表达成互信息熵，等于先验不确定性与损失熵的差值。先验不确定性可以是被测量的客观随机变化性，也可以是对被测量的一种主观不确定性。先验不确定性根据测量者的先验知识或最大熵原理来计算。损失熵反映了测量仪器和过程的特性。先验不确定和损失熵可以采用连续熵形式或离散熵形式来计算，而且依据一定的方法，连续熵形式和离散熵形式可以相互转化。2个例子显示了测量信息的计算方法。

Calculation of Measurement Information

The measurement is a process to capture information, and measurement information can be quantified by the amount of uncertainty reduced from the measurement action. The measurement information can be formalized by interactive information entropy which is equal to prior uncertainty subtracting losing entropy. Prior uncertainty is objective uncertainty or subjective uncertainty for the measured value before measurement, and can be calculated according to prior knowledge or principle of maximum of entropy. Losing entropy reflects the properties of measurement instruments and process. Both prior uncertainty and losing entropy can be calculated through the form of discrete entropy or continuous entropy. The process to calculate measurement information is demonstrated by two examples.