Difference between revisions of "Multinomial"
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| − | [[ | + | [[Category: Multivariate Distribution Functions]] |
| − | [[Category:Doc Status C]] <!-- For Lumina use, do not change --> | + | [[Category: Multivariate Distributions library functions]] |
| + | [[Category: Doc Status C]] <!-- For Lumina use, do not change --> | ||
| − | = Multinomial( N,theta | + | == Multinomial(N, theta, I) == |
Returns the Multinomial Distribution. | Returns the Multinomial Distribution. | ||
| − | The multinomial distribution is a generalization of the [[Binomial]] distribution to | + | The multinomial distribution is a generalization of the [[Binomial]] distribution to «N» possible outcomes. For example, if you were to roll a fair die «N» times, an outcome would be the number of times each of the six numbers appears. Theta would be the probability of each outcome, where <code>Sum(theta, I) = 1</code>, and index «I» is the list of possible outcome. If «theta» doesn't sum to 1, it is normalized. |
| − | Each sample is a vector indexed by | + | Each sample is a vector indexed by «I» indicating the number of times the corresponding outcome (die number) occurred during that sample point. Each sample will have the property that <code>Sum(result, I) = N</code>. |
| − | + | [[Syntax]]: | |
| + | :[[Multinomial]](N, theta: postiive; I: Index) | ||
| − | Multivariate Distributions.ana | + | == Library == |
| − | + | Multivariate Distributions library functions ([[media:Multivariate Distributions.ana |Multivariate Distributions.ana]]) | |
| − | + | :Use [[File menu|File]] → '''Add Library...''' to add this library | |
| + | == See Also == | ||
* [[Binomial]] | * [[Binomial]] | ||
| + | * [[Sum]] | ||
| + | * [[Multivariate distributions]] | ||
| + | * [[media:Multivariate Distributions.ana |Multivariate Distributions.ana]] | ||
Latest revision as of 22:10, 24 May 2016
Multinomial(N, theta, I)
Returns the Multinomial Distribution.
The multinomial distribution is a generalization of the Binomial distribution to «N» possible outcomes. For example, if you were to roll a fair die «N» times, an outcome would be the number of times each of the six numbers appears. Theta would be the probability of each outcome, where Sum(theta, I) = 1, and index «I» is the list of possible outcome. If «theta» doesn't sum to 1, it is normalized.
Each sample is a vector indexed by «I» indicating the number of times the corresponding outcome (die number) occurred during that sample point. Each sample will have the property that Sum(result, I) = N.
- Multinomial(N, theta: postiive; I: Index)
Library
Multivariate Distributions library functions (Multivariate Distributions.ana)
- Use File → Add Library... to add this library
See Also
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