Everything about Discrete Random Variable totally explained
In
probability theory, a
probability distribution is called
discrete if it's characterized by a
probability mass function. Thus, the distribution of a
random variable X is discrete, and
X is then called a
discrete random variable, if
»
as
u runs through the set of all possible values of
X.
If a random variable is discrete, then the
set of all values that it can assume with non-zero probability is
finite or
countably infinite, because the sum of uncountably many positive
real numbers (which is the
least upper bound of the set of all finite partial sums) always diverges to infinity.
Typically, this set of possible values is a topologically discrete set in the sense that all its points are
isolated points. But, there are discrete random variables for which this countable set is
dense on the real line.
The
Poisson distribution, the
Bernoulli distribution, the
binomial distribution, the
geometric distribution, and the
negative binomial distribution are among the most well-known discrete probability distributions.
Alternative description
Equivalently to the above, a discrete random variable can be defined as a random variable whose
cumulative distribution function (cdf) increases only by
jump discontinuities — that is, its cdf increases only where it "jumps" to a higher value, and is constant between those jumps. The points where jumps occur are precisely the values which the random variable may take. The number of such jumps may be finite or
countably infinite. The set of locations of such jumps need not be topologically discrete; for example, the cdf might jump at each
rational number.
Representation in terms of indicator functions
For a discrete random variable
X, let
u0,
u1, ... be the values it can take with non-zero probability. Denote
»
except on a set of probability zero, where
and
is the
indicator function of
A. This may serve as an alternative definition of discrete random variables.
Further Information
Get more info on 'Discrete Random Variable'.
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