Randomness
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The application of the uncertainty modell randomnes presuppose the existence of data with the property randomness. The uncertainty model randomness bases on the mathematical theory of probability.
Probability can be divided into two related concepts:
- Aleatory probability, which represents the likelihood of future events whose occurrence is governed by some random physical phenomenon. This concept can be further divided into physical phenomena that are predictable, in principle, with sufficient information, and phenomena which are essentially unpredictable.
- Epistemic probability, which represents uncertainty about propositions when one lacks complete knowledge of causative circumstances. Such propositions may be about past or future events, but need not be.
Randomness can be described by probability distribution functions and probability density functioncs. For the purposes of simulation it is necessary to have a large supply of random numbers, or means to generate them on demand. This is usually done with the aid of probability distributions functions. A probability distribution is a function that assigns probabilities to events. For any set of events there are many ways to assign probabilities, so the choice of one distribution or another is equivalent to making different assumptions about the events or propositions in question.
References
- Benjamin, JR, and Cornell, CA (1970) Probability, Statistics and Decision for Civil Engineers, McGraw-Hill, New York.
