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FactorialMoment [data,r]

gives the order r factorial moment of data.

FactorialMoment [data,{r1,,rm}]

gives the order {r1,,rm} multivariate factorial moment of data.

FactorialMoment [dist,]

gives the factorial moment of the distribution dist.

FactorialMoment [r]

represents the order r formal factorial moment.

Details
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
Basic Uses  
Array Data  
Image and Audio Data  
Distribution and Process Moments  
Formal Moments  
Applications  
Properties & Relations  
Neat Examples  
See Also
Related Guides
History
Cite this Page

FactorialMoment [data,r]

gives the order r factorial moment of data.

FactorialMoment [data,{r1,,rm}]

gives the order {r1,,rm} multivariate factorial moment of data.

FactorialMoment [dist,]

gives the factorial moment of the distribution dist.

FactorialMoment [r]

represents the order r formal factorial moment.

Details

  • Factorial moments are defined using FactorialPower [x,r] given by .
  • For scalar order r and data being an array :
  • x in TemplateBox[{Vectors, paclet:ref/Vectors}, RefLink, BaseStyle -> {3ColumnTableMod}][n] sum of r^(th) factorial powers »
    x in TemplateBox[{Matrices, paclet:ref/Matrices}, RefLink, BaseStyle -> {3ColumnTableMod}][{n,m}] columnwise sum of r^(th) factorial powers »
    x in TemplateBox[{Arrays, paclet:ref/Arrays}, RefLink, BaseStyle -> {3ColumnTableMod}][{n_(1),...,n_(k)}] columnwise sum of r^(th) factorial powers »
  • FactorialMoment [x,r] is equivalent to ArrayReduce [FactorialMoment[#,r]&,x,1].
  • For vector order {r1,,rm} and data being array :
  • x in TemplateBox[{Matrices, paclet:ref/Matrices}, RefLink, BaseStyle -> {3ColumnTableMod}][{n,m}] sum the rj^(th) factorial power in the j^(th) column
    x in TemplateBox[{Arrays, paclet:ref/Arrays}, RefLink, BaseStyle -> {3ColumnTableMod}][{n_(1),...,n_(k)}] sum the rj^(th) factorial power in the j^(th) column »
  • FactorialMoment [x,{r1,,rm}] is equivalent to ArrayReduce [FactorialMoment[#,{r1,,rm}]&,x,{{1},{2}}].
  • FactorialMoment handles both numerical and symbolic data.
  • The data can have the following additional forms and interpretations:
  • Association the values (the keys are ignored) »
    WeightedData weighted mean, based on the underlying EmpiricalDistribution »
    EventData based on the underlying SurvivalDistribution »
    TimeSeries , TemporalData , vector or array of values (the time stamps ignored) »
    Image ,Image3D RGB channel's values or grayscale intensity value »
    Audio amplitude values of all channels »
  • For a distribution dist, the r^(th) factorial moment is given by Expectation [x(r),xdist]. »
  • For a multivariate distribution dist, the {r1,,rm}^(th) factorial moment is given by Expectation [x1(r1)xm(rm),{x1,,xm}dist]. »
  • For a random process proc, the factorial moment function can be computed for slice distribution at time t, SliceDistribution [proc,t], as mu^__r[t]=FactorialMoment[SliceDistribution [proc,t],r]. »
  • FactorialMoment [r] can be used in such functions as MomentConvert and MomentEvaluate , etc. »

Examples

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Basic Examples  (2)

Compute factorial moment from data:

Use symbolic data:

Compute the second factorial moment of a discrete univariate distribution:

The factorial moment for a multivariate distribution:

Scope  (20)

Basic Uses  (5)

Exact input yields exact output:

Approximate input yields approximate output:

Find factorial moments of WeightedData :

Find a factorial moment of EventData :

Find a factorial moment of TimeSeries :

Factorial moment depends only on the values:

Array Data  (4)

For a matrix, FactorialMoment gives columnwise moments:

For an array, FactorialMoment gives columnwise moments at the first level:

Multivariate FactorialMoment for an array:

Works with large arrays:

When the input is an Association , FactorialMoment works on its values:

SparseArray data can be used just like dense arrays:

Image and Audio Data  (2)

Channelwise factorial moment of an RGB image:

Factorial moment intensity value of a grayscale image:

On audio objects, FactorialMoment works channelwise:

Distribution and Process Moments  (5)

Scalar factorial moment for univariate distributions:

Scalar factorial moment for multivariate distributions:

Joint factorial moment for multivariate distributions:

Compute a factorial moment for a symbolic order r:

A factorial moment may only evaluate for specific orders:

A factorial moment may only evaluate numerically:

Factorial moments for derived distributions:

Data distribution:

Factorial moment function for a random process:

Find a factorial moment of TemporalData at some time t=0.5:

Find the corresponding factorial moment function together with all the simulations:

Formal Moments  (4)

TraditionalForm formatting for formal moments:

Convert combinations of formal moments to an expression involving FactorialMoment :

Evaluate an expression involving formal moments TemplateBox[{2}, FactorialMoment]+TemplateBox[{3}, FactorialMoment] for a distribution:

Evaluate for data:

Find a sample estimator for an expression involving FactorialMoment :

Evaluate the resulting estimator for data:

Applications  (4)

Estimate parameters of a distribution using the method of factorial moments:

Compare data and the estimated parametric distribution:

Reconstruct probability mass function from the sequence of factorial moments:

Find the factorial moment-generating function (FMGF):

Use equivalence of the FMGF and the probability generating function:

Verify that factorial moments of the found distribution match the originals:

Compute a moving factorial moment for some data:

Use the window of length .1:

Compute factorial moments for slices of a collection of paths of a random process:

Choose a few slice times:

Plot factorial moments over these paths:

Properties & Relations  (5)

Factorial moment is equivalent to an expectation of FactorialPower :

First factorial moment is equivalent to Mean :

FactorialMoment can be computed from Moment through mu^__r=sum_(k=1)^rTemplateBox[{r, k}, StirlingS1]mu_k :

MomentConvert produces the same result:

Moment can be computed from FactorialMoment through mu_r=sum_(k=0)^rmu^__k TemplateBox[{r, k}, StirlingS2]:

MomentConvert produces the same result:

The multivariate factorial moment of an array of depth has depth :

Neat Examples  (1)

The distribution of FactorialMoment estimates for 30, 100, and 300 samples:

Wolfram Research (2010), FactorialMoment, Wolfram Language function, https://reference.wolfram.com/language/ref/FactorialMoment.html (updated 2024).

Text

Wolfram Research (2010), FactorialMoment, Wolfram Language function, https://reference.wolfram.com/language/ref/FactorialMoment.html (updated 2024).

CMS

Wolfram Language. 2010. "FactorialMoment." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2024. https://reference.wolfram.com/language/ref/FactorialMoment.html.

APA

Wolfram Language. (2010). FactorialMoment. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/FactorialMoment.html

BibTeX

@misc{reference.wolfram_2025_factorialmoment, author="Wolfram Research", title="{FactorialMoment}", year="2024", howpublished="\url{https://reference.wolfram.com/language/ref/FactorialMoment.html}", note=[Accessed: 04-December-2025]}

BibLaTeX

@online{reference.wolfram_2025_factorialmoment, organization={Wolfram Research}, title={FactorialMoment}, year={2024}, url={https://reference.wolfram.com/language/ref/FactorialMoment.html}, note=[Accessed: 04-December-2025]}

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