We extend algorithmic conservation inequalities to probability measures. The amount of self information of a probability measure cannot increase when submitted to randomized processing. This includes (potentially non-computable) measures over finite sequences, infinite sequences, and $T_0$, second countable topologies. One example is the convolution of signals over real numbers with probability kernels. Thus the smoothing of any signal due We show that given a quantum measurement, for an overwhelming majority of pure states, no meaningful information is produced.

翻译：我们把算法保护不平等扩大到概率计量。 提交随机处理时, 概率计量的自我信息量无法增加。 这包括( 可能无法计算) 定序测量、 无限序列和 $T_ 0$, 第二个可计数的表层。 一个例子是, 信号与实际数字的组合, 带有概率内核 。 因此, 任何到期信号的平滑 表明, 对于绝大多数纯国家来说, 量度测量不会产生有意义的信息 。</s>