Continuous Moments (Weibull) (G Dataflow)

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Last Modified: January 12, 2018

Calculates the mean and variance of a Weibull-distributed variate.

b

Scale parameter of the random variable.

This input must be greater than 0.

Default: 1

c

Shape parameter of the random variable.

This input must be greater than 0.

Default: 3

error in

Error conditions that occur before this node runs.

The node responds to this input according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error	error in contains an error

If no error occurred before the node runs, the node begins execution normally. If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.	If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Default: No error

mean

Expected mean of the random variable.

variance

Expected variance of the random variable.

error out

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

error in does not contain an error	error in contains an error

If no error occurred before the node runs, the node begins execution normally. If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.	If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Algorithm for Calculating the Mean and Variance of a Weibull-Distributed Variate

This node uses the following equations to calculate the mean and variance of a Weibull-distributed variate.

mean = b Γ (\frac{c + 1}{c})

variance = b^{2} {Γ (\frac{c + 2}{c}) - {[Γ (\frac{c + 1}{c})]}^{2}}

where b is the scale parameter of the random variable and c is the shape parameter of the random variable.

Where This Node Can Run:

Desktop OS: Windows

FPGA: Not supported

Web Server: Not supported in VIs that run in a web application

Table Of Contents

b

c

error in

mean

variance

error out

Algorithm for Calculating the Mean and Variance of a Weibull-Distributed Variate

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