Discrete Probability Function Structure

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):
  • a Discrete Probability Software Function.
  • a Probability Table / Probability Distribution Multiset / Probability Distribution Table.
  • a Joint Probability Structure.
  • …
• Counter-Example(s):
  • a Continuous Probability Density Structure.
• See: Gini Diversity Index, Discrete Random Variable.

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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.

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