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Probability Mass Function





MATHEMATICAL DESCRIPTION


Suppose that ''X'' is a discrete random variable, taking values on some Countable Sample Space  ''S'' ⊆ R. Then the probability mass function  ''f''''X''(''x'')  for ''X'' is given by
:f_X(x) = \begin{cases} \Pr(X = x), &x\in S,\0, &x\in \mathbb{R}\backslash S.\end{cases}
Note that this explicitly defines  ''f''''X''(''x'')  for all Real Number s, including all values in R that ''X'' could never take; indeed, it assigns such values a probability of zero. (Alternatively, think of  Pr(''X'' = ''x'')  as 0 when  ''x'' ∈ R\''S''.)

The discontinuity of probability mass functions reflects the fact that the cumulative distribution function of a discrete random variable is also discontinuous. Where it is differentiable (i.e. where ''x'' ∈ R\''S'') the derivative is zero, just as the probability mass function is zero at all such points.


EXAMPLES


A simple example of a probability mass function is the following. Suppose that ''X'' is the outcome of a single coin toss, assigning 0 to tails and 1 to heads. The probability that ''X'' = ''x'' is just 0.5 on the state space {0, 1} (this is a Bernoulli Random Variable ), and hence the probability mass function is
:f_X(x) = \begin{cases} rac{1}{2}, &x \in \{0, 1\},\0, &x \in \mathbb{R}\backslash\{0, 1\}.\end{cases}

Probability mass functions may also be defined for any discrete random variable, including Constant , Binomial (including Bernoulli ), Negative Binomial , Poisson , Geometric and Hypergeometric random variables.