Probability Distribution

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Definition of 'Probability Distribution'

A probability distribution is a function that describes the possible outcomes of a random variable and their associated probabilities. It is a mathematical representation of uncertainty.

Probability distributions are used in many fields, such as statistics, finance, and engineering. In finance, probability distributions are used to model the uncertainty of future events, such as the returns on an investment.

There are many different types of probability distributions. Some of the most common include the normal distribution, the binomial distribution, and the Poisson distribution.

The normal distribution is a bell-shaped curve that is used to model a wide variety of data. The binomial distribution is used to model the number of successes in a sequence of independent experiments. The Poisson distribution is used to model the number of events that occur in a fixed interval of time or space.

Probability distributions are important because they allow us to quantify uncertainty. By understanding the possible outcomes of a random variable and their associated probabilities, we can make better decisions under uncertainty.

Here are some additional details about probability distributions:

* A probability distribution is a function that assigns a probability to each possible outcome of a random variable.
* The probability of an outcome is a number between 0 and 1, where 0 indicates that the outcome is impossible and 1 indicates that the outcome is certain.
* The sum of the probabilities of all possible outcomes must equal 1.
* Probability distributions can be used to model a wide variety of random phenomena.
* Some of the most common probability distributions include the normal distribution, the binomial distribution, and the Poisson distribution.
* Probability distributions are important because they allow us to quantify uncertainty. By understanding the possible outcomes of a random variable and their associated probabilities, we can make better decisions under uncertainty.

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