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Random structure function

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The random structure function is the third component of the Bernoulli space which constitutes the stochastic model within Bernoulli stochastics.[1] The Bernoulli space describes the transition from past to future. The determinate past is represented by a variable D which is called deterministic variable, because its value is fixed. The future represented by the variable X is subject to randomness and X is therefore called random variable. The random variable X may adopt one of a set of different values according to a random law which depends on the actual initial conditions given by the value d of the deterministic variable. The random law does not only fix the range of variability of X but also the probability of the future events which are given by subsets of the range of variability of X.

Probability distribution[edit]

The random variable X stands for the future indeterminate outcome of a process. If the process is repeated then different outcomes will occur according to a random law that depends on the actual initial conditions given by the value d of the deterministic variable D. The random variable X under the condition d is denoted where the set of possible initial conditions is given by the ignorance space . The random structure function assigns to each subset of the ignorance space a probability distribution.

Let be a subset of the ignorance space then the corresponding probability distribution is obtained from the images of the singletons as follows:

It follows that for any future event E, we have:

Thus, the probability distribution of the random variable is given by the mean of the probability distributions of the random variables .

References[edit]

  1. Elart von Collani (ed.), Defining the Science Stochastics, Heldermann Verlag, Lemgo, 2004.


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