random variable

Then this is done again with a new set of random variables.

This set of random variables represents the risk at hand.

It models the state of a system with a random variable that changes through time.

The random variable itself should be thought of as the process how the observation comes about.

Then the values taken by the random variable are directions.

Its events and random variables are the same as on the usual circle.

Let random variable Q be the number of edges cut.

There is a probability of that this random variable will have the value 1.

This random variable tells whether or not a particular bit will be modified.

Recent results shows that such behavior is shared by other functions of independent random variables.

Expected value, the mean of a random variable's probability distribution.

They play a similar role as the characteristic functions for random variable.

The choice of the random variables X was rather natural in this setup.

Another one is that the different random variables (or observations) must be independent of each other.

Let be a discrete random variable that takes values 1,2,.

Suppose that the random variables X have common expected value zero.

There are similar counterexamples for more than two random variables.

A random variable is used in mathematics to study probability theory.

This is the "average" direction of the angular random variables.

For example, suppose the profits are x, which might be a random variable.

That definition is exactly equivalent to the one above when the values of the random variables are real numbers.

Thus is a random variable representing what has evolved to after generations.

Objects are often classified based on a continuous random variable.

Characteristic functions can also be used to find moments of a random variable.

A risk measure is defined as a mapping from a set of random variables to the real numbers.