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The 20 pages that link to Probability mass function, each with the reason it gives.

• Expected valueRelated: Multiplying each value by its probability and summing computes the expectation.

• Random variableBroader topic: It describes value probabilities when the random variable has a discrete range.

• Probability distributionBroader topic: It represents distributions whose probability is concentrated on countable values.

• FunctionBroader topic: It turns a function's input-output rule into a description of discrete uncertainty.

• Probability density functionCompared with: Unlike a density, its value at an outcome is already a probability.

• Binomial distributionRelated: It gives the probability of every possible success count.

• Probability measureCompared with: For discrete spaces, point probabilities can represent the measure directly.

• Likelihood functionRelated: For discrete data, likelihood is the probability mass function evaluated at the observation and treated as a function of parameters.

• Poisson distributionNarrower topic: The Poisson distribution is specified by this kind of function.

• Cumulative distribution functionRelated: For discrete variables, cumulative sums of point probabilities produce the CDF.

• Geometric distributionRelated: Its formula gives the probability of each possible waiting time.

• Bernoulli distributionNarrower topic: The Bernoulli distribution is specified by assigning mass p to 1 and 1−p to 0.

• Probability-generating functionRelated: Its probabilities become the coefficients of the generating function.

• Independent random variablesRelated: For discrete variables, independence means their joint mass function factors pointwise.

• Hypergeometric distributionRelated: The hypergeometric formula gives the probability of each possible success count.

• Discrete uniform distributionRelated: It expresses the distribution by assigning probability 1/n to each allowed value.

• Outcome (probability)Related: For discrete outcomes, it specifies their probability weights.

• De Moivre–Laplace theoremRelated: The theorem approximates binomial probability masses with values from a normal density.

• Le Cam's theoremRelated: The theorem compares distributions over the integer-valued outcomes of the sum.

• Family (statistics)Related: Discrete families share a mass-function form as their parameters vary.