The entropy of a mixture of probability distributions

If a message can have n different values and all values are equally probable, then the entropy of the message is log(n). In the present paper, we investigate the expectation value of the entropy, for arbitrary probability distribution. For that purpose, we apply mixed probability distributions. The mixing distribution is represented by a point on an infinite dimensional hypersphere in Hilbert space. During an `arbitrary' calculation, this mixing distribution has the tendency to become uniform over a flat probability space of ever decreasing dimensionality. Once such smeared-out mixing distribution is established, subsequent computing steps introduce an entropy loss expected to equal $\\frac{1}{m+1} + \\frac{1}{m+2} + ... + \\frac{1}{n}$, where n is the number of possible inputs and m the number of possible outcomes of the computation.