Discrete CDF VI

Owning Palette: Probability VIs

Requires: Full Development System

Computes the discrete cumulative distribution function (CDF), or the probability that the random variate X, where X describes the selected distribution type, takes on a value less than or equal to x. You must manually select the polymorphic instance to use.

Details  Example

Use the pull-down menu to select an instance of this VI.

Select an instance Bernoulli CDFBinomial CDFGeometric CDFHypergeometric CDFNeg Binomial CDFPoisson CDFUniform (Discrete) CDF

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Bernoulli CDF

	x is the number of successes and must be 0 or 1.
	p is the probability of success (probability that x = 1) and must be in the interval [0,1].
	cdf(x) returns the cumulative probability that the random variate X, where X describes the selected distribution type, has a value less than or equal to x.
	error returns any error or warning from the VI. You can wire error to the Error Cluster From Error Code VI to convert the error code or warning into an error cluster.

Binomial CDF

X represents a binomial-distributed variate: the number of successes in n independent Bernoulli trials. The Bernoulli probability parameter p is the probability of success of each trial or experiment.

	x is the number of successes and must be in the interval [0,n].
	n is the number of independent Bernoulli trials.
	p is the probability of success of each Bernoulli trial and must be in the interval [0,1].
	cdf(x) returns the cumulative probability that the random variate X, where X describes the selected distribution type, has a value less than or equal to x.
	error returns any error or warning from the VI. You can wire error to the Error Cluster From Error Code VI to convert the error code or warning into an error cluster.

Geometric CDF

X represents a geometric-distributed variate: given a sequence of n independent Bernoulli trials, X is the number of trials (or failures) before the first success. The Bernoulli probability parameter p is the probability of success of each trial or experiment.

	n is the number of independent Bernoulli trials.
	p is the probability of success of each Bernoulli trial and must be in the interval [0,1].
	cdf(n) is the cumulative probability Prob[X ≤ n].
	error returns any error or warning from the VI. You can wire error to the Error Cluster From Error Code VI to convert the error code or warning into an error cluster.

Hypergeometric CDF

X represents a hypergeometric-distributed variate: the number of successes when n items are drawn from an M-sized population of which k elements are successes.

	x is the number of successes out of the sample of n items drawn from the population.
	M is the number of elements in the population.
	k is the number of successes in the population.
	n is the number of items drawn without replacement.
	cdf(x) returns the cumulative probability that the random variate X, where X describes the selected distribution type, has a value less than or equal to x.
	error returns any error or warning from the VI. You can wire error to the Error Cluster From Error Code VI to convert the error code or warning into an error cluster.

Neg Binomial CDF

X represents a negative binomial-distributed variate: the number of failures before the x^th success in a sequence of Bernoulli trials. The Bernoulli probability parameter p is the probability of success of each trial or experiment.

	y is the number of failures before the xth success.
	x is the number of successes and must be in the interval [0,inf].
	p is the probability of success of each Bernoulli trial and must be in the interval [0,1].
	cdf(y) is the cumulative probability Prob[X ≤ y].
	error returns any error or warning from the VI. You can wire error to the Error Cluster From Error Code VI to convert the error code or warning into an error cluster.

Poisson CDF

X represents a Poisson-distributed variate which takes on discrete, non-negative values (X = 0, 1, 2, 3, ...) and often represents the number of events occurring within a specific time interval. The parameter lambda represents the average number of occurrences expected to occur within the specific time interval.

	x is the number of events and must be greater than or equal to 0.
	lambda is the average number of events expected to occur within a specific time interval.
	cdf(x) returns the cumulative probability that the random variate X, where X describes the selected distribution type, has a value less than or equal to x.
	error returns any error or warning from the VI. You can wire error to the Error Cluster From Error Code VI to convert the error code or warning into an error cluster.

Uniform (Discrete) CDF

X represents a discrete uniform-distributed variate where every value in the range of integers in the interval [1,n] is equally likely to occur.

	x is an integer in the range of [0,n].
	n is the maximum value of the uniform variate.
	cdf(x) returns the cumulative probability that the random variate X, where X describes the selected distribution type, has a value less than or equal to x.
	error returns any error or warning from the VI. You can wire error to the Error Cluster From Error Code VI to convert the error code or warning into an error cluster.

Discrete CDF Details

When you use the Bernoulli instance of this function, X represents a Bernoulli-distributed variate with one of two possible outcomes: success (x = 1) or failure (x = 0). The Bernoulli probability parameter p is the probability of success of a single trial or experiment.

Example

Refer to the Display Discrete Probability Distributions VI in the labview\examples\Mathematics\Probability and Statistics directory for an example of using the Discrete CDF VI.

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