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In probability and statistics, a probability mass function is a function that gives the probability that a discrete random variable is exactly equal to some value. Sometimes it is also known as the discrete density function. The probability mass function is often the primary means of defining a discrete probability distribution, and such functions exist for either scalar or multivariate random variables whose domain is discrete. The value of the random variable having the largest probability mass is called the mode.

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rdf:type	disease
rdfs:label	Probability mass function (en)
rdfs:comment	In probability and statistics, a probability mass function is a function that gives the probability that a discrete random variable is exactly equal to some value. Sometimes it is also known as the discrete density function. The probability mass function is often the primary means of defining a discrete probability distribution, and such functions exist for either scalar or multivariate random variables whose domain is discrete. The value of the random variable having the largest probability mass is called the mode. (en)
sameAs	Probability mass function Probability mass function
dbp:wikiPageUsesTemplate	dbt:Cite_book dbt:Clear dbt:Equation_box_1 dbt:Main dbt:Reflist dbt:Short_description dbt:Pb dbt:Dice dbt:Theory_of_probability_distributions
Subject	Types of probability distributions
gold:hypernym	Function
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Probability_mass_function?oldid=1063984048&ns=0
Wikipage page ID	154725 (xsd:integer)
page length (characters) of wiki page	10093 (xsd:nonNegativeInteger)
Wikipage revision ID	1063984048 (xsd:integer)
has abstract	In probability and statistics, a probability mass function is a function that gives the probability that a discrete random variable is exactly equal to some value. Sometimes it is also known as the discrete density function. The probability mass function is often the primary means of defining a discrete probability distribution, and such functions exist for either scalar or multivariate random variables whose domain is discrete. A probability mass function differs from a probability density function (PDF) in that the latter is associated with continuous rather than discrete random variables. A PDF must be integrated over an interval to yield a probability. The value of the random variable having the largest probability mass is called the mode. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Probability_mass_function
is Wikipage redirect of	Discrete probability space Probability mass
is Link from a Wikipage to another Wikipage of	Flory–Schulz distribution List of statistics articles Location parameter Location–scale family Logarithmic distribution Logistic regression Normalizing constant Ancillary statistic Arbitrarily varying channel Belief propagation Bernoulli distribution Bernoulli process Binomial distribution Binomial test Boltzmann machine Borel distribution Monotone likelihood ratio Cantor distribution Catalog of articles in probability theory Categorical distribution Generalized linear model Generating function Geometric Poisson distribution German tank problem Glossary of engineering: A–L Glossary of engineering: M–Z Jackson network Jurimetrics Dirichlet process Discrete Fourier transform Kullback–Leibler divergence Kurtosis Credal set Cumulative distribution function De Moivre–Laplace theorem Degenerate distribution Huffman coding Hypergeometric distribution Lindley's paradox Linear belief function List of mathematical abbreviations List of probability topics Complex random variable Conditional entropy Conditional mutual information Conjugate prior Continuous Bernoulli distribution Continuous or discrete variable Convolution of probability distributions Conway–Maxwell–Poisson distribution Conway–Maxwell–binomial distribution Dyadic distribution Hermite distribution List of convolutions of probability distributions Neyman–Pearson lemma Notation in probability and statistics End-to-end delay Entropy (information theory) Error exponent Fisher information Fisher information metric Functional derivative Gauss–Kuzmin distribution Illustration of the central limit theorem Kernel (statistics) Exponential family Extended negative binomial distribution Hammersley–Clifford theorem Information content Information theory Joint probability distribution Kraft–McMillan inequality Kronecker delta Mode (statistics) Moment-generating function Estimation theory

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