In
probability theory, every
random variable may be attributed to a function defined on a state space equipped with a probability distribution that assigns a
probability to every
subset (more precisely every measurable subset) of its
state space in such a way that the
probability axioms are satisfied. That is, probability distributions are
probability measures defined over a state space instead of the sample space. A random variable then defines a probability measure on the sample space by assigning a subset of the sample space the probability of its inverse image in the state space. In other words the probability distribution of a random variable is the
push forward measure of the probability distribution on the state space.
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