probability distribution


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Probability distribution

A function that describes all the values a random variable can take and the probability associated with each. Also called a probability function.

Probability Distribution

All the possible values a random variable can take under a given set of circumstances, as well as the probability that it will take each value. A normal probability is a bell curve.

probability distribution

The distribution of possible outcomes to an event along with the probability of each potential outcome. This statistical tool is used to measure the risk associated with events such as shooting craps, playing cards, or investing in securities.
References in periodicals archive ?
Using these Excel functions assumes that the inherent uncertainty in these two factors is stochastic (random) in nature, and the probability distribution for each of these random variables is normal (Gornik-Tomaszewski, 2014).
Once the impedance and probability distribution are derived, the equivalent model is saved by selecting the Save Model button.
The goal of this study was to assess the influence of different probability distributions and their combinations to the accuracy of classifiers constructed on their basis both for separate classification of single images and using data fusion.
Probability distribution of annual maximum, mean, and minimum streamflows in the United States.
This can be achieved by using certain tools, such as Monte Carlo simulation, which mathematically compute future scenarios and tell us how likely are the occurrences of the possible outcomes, their probability distributions and risks related to them.
Input the neural network prediction results into Bayesian networks and calculate posterior probability distribution under this evidence.
For one-time projects or in situations with limited budgets, it's possible to use Excel to create a Monte Carlo simulation using three of the most common probability distributions.
The full joint probability distribution for X is given as a product of local interactions by:
BNs address the problems of storing and representing the joint probability distribution of a large number of random variables and doing Bayesian inference with these variables.
Simulation should be repeated based on different random selections from the library collection (usually between 80 to 500 times), and the probability distribution of the output shall be generated based on the results of all the performed simulations.
We want to estimate a probability distribution F that describes the actual values corresponding to possible samples x = ([x.
The LLN ensures that as the number of random samples collected from a probability distribution is increased, the sample mean converges to the true population mean, and the CLT guarantees that the sampling distribution of the mean will be Gaussian, provided there are a sufficient number of independent observations.

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