The random variable X stands for the indeterminate future outcome of a process.

A continuous-time stochastic process assigns a random variable X to each point t 0 in time.

Then we apply the law of total expectation to each term by conditioning on the random variable X:

Consider a random variable X which can take on the values 0, 1, 2.

The probability mass function of the random variable X may be depicted by the following bar graph:

Given a sampling mechanism for the random variable X, we model to be equal to .

A random variable X will adopt one value of the set of possible values.

The Fisher information is not a function of a particular observation, as the random variable X has been averaged out.

Suppose you want to sample some random variable X with distribution f(x).

For the random variable X, one takes the conformal factor of the given metric with respect to the flat one.