Discrete Probability Function Structure: Difference between revisions
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A [[Discrete Probability Function Structure]] is a [[probability function structure]] for a [[discrete random experiment]]. | A [[Discrete Probability Function Structure]] is a [[probability function structure]] for a [[discrete random experiment]]. | ||
* <B>AKA:</B> [[Discrete Probability Function Structure|Categorical Distribution]] | * <B>AKA:</B> [[Discrete Probability Function Structure|Categorical Distribution]]. | ||
* <B>Context:</B> | * <B>Context:</B> | ||
** It can be produced by [[Discrete Probability Function Creation Task]]. | ** It can be produced by [[Discrete Probability Function Creation Task]]. | ||
Latest revision as of 17:53, 4 October 2023
A Discrete Probability Function Structure is a probability function structure for a discrete random experiment.
- AKA: Categorical Distribution.
- Context:
- It can be produced by Discrete Probability Function Creation Task.
- It can range from being a Binomial Probability Function Structure to being a Multinomial Probability Function Structure.
- …
- Example(s):
- Counter-Example(s):
- See: Gini Diversity Index, Discrete Random Variable.
References
2014
- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Multinomial_distribution Retrieved:2014-10-29.
- … Note that, in some fields, such as natural language processing, the categorical and multinomial distributions are conflated, and it is common to speak of a "multinomial distribution" when a categorical distribution is actually meant. This stems from the fact that it is sometimes convenient to express the outcome of a categorical distribution as a "1-of-K" vector (a vector with one element containing a 1 and all other elements containing a 0) rather than as an integer in the range [math]\displaystyle{ 1 \dots K }[/math]; in this form, a categorical distribution is equivalent to a multinomial distribution over a single trial.