Conventionally, it is known as discrete probability, as an example, the binomial probability distribution. For absolute probability (quantized probability), the random variable only takes on positive integer values, e.g. 0, 1, 2, 3, 4, … The lowest bound of zero is sometime ignored but the largest upper bound approaching infinity is never used for all practical purposes. In practice, by the central limit theorem, most absolute probability functions are often approximated by the continuous probability of a normally distributed Gaussian error function whose area integral is set exactly unity.
Unfortunately, the domain of continuous functions includes irrational numbers. These numbers possess built-in intrinsic probabilities of absolute unpredictability. As an example, the decimal expansion of p(see http://en.wikipedia.org/wiki/Pi)would indicate. The probability of predicting which decimal digit among 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 will appear next changes according to its previous appearance in the decimal sequence if not then their probabilities are all equal to zero.


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