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Using JMP > Formula Functions Reference > Discrete Probability Functions
Publication date: 07/30/2020

Discrete Probability Functions

Gamma Poisson Distribution Probability Functions

For more information about syntax for the Gamma Poisson Distribution probability functions, see Discrete Probability Functions in the JSL Syntax Reference.

Gamma Poisson Probability

Returns the probability or pmf that a gamma-Poisson distributed random variable is equal to x. In general, the gamma Poisson functions accept arguments that are the mean parameter lambda, the overdispersion parameter sigma, and the count of interest x. When the overdispersion is equal to one, the Gamma Poisson reduces to a Poisson(lambda) distribution.

Gamma Poisson Distribution

Returns the probability that a gamma-Poisson distributed random variable is less than or equal to x. In general, the gamma Poisson functions accept arguments that are the mean parameter lambda, the overdispersion parameter sigma, and the count of interest x.

Gamma Poisson Quantile

Returns the smallest integer quantile for which the cumulative probability of the Gamma Poisson (lambda, sigma) distribution is larger than or equal to p.

Binomial Distribution Probability Functions

For more information about syntax for the Binomial Distribution probability functions, see Discrete Probability Functions in the JSL Syntax Reference.

Binomial Distribution

Returns the probability that an observation from a binomial distribution with parameters p and n is less than or equal to k. In general, the binomial functions accept arguments that are the probability of success p (the event of interest), the number of trials n, and the number of successes k.

Binomial Probability

Computes the probability that a random variable from a binomial distribution is equal to k. In general, the binomial functions accept arguments that are the probability of success p (the event of interest), the number of trials n, and the number of successes k.

Binomial Quantile

Returns the smallest integer quantile for which the cumulative probability of the Binomial (p, n) distribution is larger than or equal to the specified probability.

Negative Binomial Distribution Probability Functions

For more information about syntax for the Negative Binomial Distribution probability functions, see Discrete Probability Functions in the JSL Syntax Reference.

Neg Binomial Distribution

Returns the probability that a negative binomially distributed random variable is less than or equal to k, where the probability of success is p, and the number of successes is n.

Neg Binomial Probability

Returns the probability that a negative binomially distributed random variable is equal to k, where the probability of success is p, and the number of successes is n.

Beta Binomial Distribution Probability Functions

For more information about syntax for the Beta Binomial Distribution probability functions, see Discrete Probability Functions in the JSL Syntax Reference.

Beta Binomial Distribution

Returns the probability or pmf that a beta binomially distributed random variable is less than or equal to x. In general, the beta binomial functions accept arguments that are the probability of success p (the event of interest), the overdispersion parameter delta, and the number of trials n. When the overdispersion parameter for the beta binomial is zero, the distribution reduces to a binomial(p, n).

Beta Binomial Probability

Returns the probability or cmf that a beta binomially distributed random variable is equal to x. When the overdispersion parameter for the beta binomial is zero, the distribution reduces to a binomial(p, n).

Beta Binomial Quantile

Returns the smallest integer quantile for which the cumulative probability of the Beta Binomial (p, n, delta) distribution is larger than or equal to the specified probability. When the overdispersion parameter for the beta binomial is zero, the distribution reduces to a binomial (p, n).

Hypergeometric Distribution Probability Functions

For more information about syntax for the Hypergeometric Distribution probability functions, see Discrete Probability Functions in the JSL Syntax Reference.

Hypergeometric Distribution

Computes the probability that a random variable from a hypergeometric distribution is less than or equal to x. The hypergeometric distribution models the total number of successes in a fixed sample drawn without replacement from a finite population. The hypergeometric functions accept as arguments the size of the population N, the total number of items with the desired characteristic in the population, K, the number of samples drawn n, and the number of successes in the sample x.

Hypergeometric Probability

Computes the probability that a random variable from a hypergeometric distribution is equal to x.

Poisson Distribution Probability Functions

For more information about syntax for the Poisson Distribution probability functions, see Discrete Probability Functions in the JSL Syntax Reference.

Poisson Distribution

Computes the probability that a random variable from a Poisson distribution with mean lambda is less than or equal to the count of interest. In general, Poisson functions accept an argument that is the count of interest, and lambda, the mean parameter.

Poisson Probability

Computes the probability that a random variable from a Poisson distribution with mean lambda is equal to the count of interest.

Poisson Quantile

Returns the smallest integer quantile for which the cumulative probability of the Poisson (lambda) distribution is larger than or equal to p.

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