User:IssaRice/Distribution of X over Y: Difference between revisions

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* A probability distribution over a finite sample space is a distribution of probabilities <math>[0,1]</math> over the sample space <math>\Omega</math>. In other words, a probability distribution is a function <math>p : \Omega \to [0,1]</math>. In the case of probability distributions, we also require some extra conditions (namely that <math>\sum_{\omega \in \Omega} p(\omega) = 1</math>).
* A probability distribution over a finite sample space is a distribution of probabilities <math>[0,1]</math> over the sample space <math>\Omega</math>. In other words, a probability distribution is a function <math>p : \Omega \to [0,1]</math>. In the case of probability distributions, we also require some extra conditions (namely that <math>\sum_{\omega \in \Omega} p(\omega) = 1</math>).
* A wealth distribution over people is a distribution of wealth (<math>\mathbf R</math>) over a set of people (<math>P</math>), that is, a function <math>w : P \to \mathbf R</math>, where <math>P</math> is a set of people. Thus, if <math>x \in P</math> then <math>w(x)</math> is <math>x</math>'s wealth.
* A wealth distribution over people is a distribution of wealth (<math>\mathbf R</math>) over a set of people (<math>P</math>), that is, a function <math>w : P \to \mathbf R</math>, where <math>P</math> is a set of people. Thus, if <math>x \in P</math> then <math>w(x)</math> is <math>x</math>'s wealth.
* A gender distribution over people assigns a gender to each person.
* A gender distribution over people assigns a gender to each person. <math>g : P \to G</math>
* A gender distribution can also be thought of as a distribution of sets of people over genders. <math>g' : G \to 2^P</math> where <math>g'(x) = g^{-1}(\{x\}) = \{p \in P : g(p) = x\}</math>


The definition above runs into trouble when the set <math>Y</math> is uncountable. In this case, we might not be able to find any function that satisfies the extra conditions we want to place on the distribution (e.g. in the case of probability distributions, we want to assign 0 to every individual outcome, but then the sum is also 0 rather than 1).
The definition above runs into trouble when the set <math>Y</math> is uncountable. In this case, we might not be able to find any function that satisfies the extra conditions we want to place on the distribution (e.g. in the case of probability distributions, we want to assign 0 to every individual outcome, but then the sum is also 0 rather than 1).


It seems like one way to get around this is to change the type of a distribution. Namely, rather than a function <math>f : Y \to X</math> we have some collection <math>\mathcal F \subseteq 2^Y</math> of subsets of <math>Y</math>, and we define a function <math>f : \mathcal F \to X</math>.
It seems like one way to get around this is to change the type of a distribution. Namely, rather than a function <math>f : Y \to X</math> we have some collection <math>\mathcal F \subseteq 2^Y</math> of subsets of <math>Y</math>, and we define a function <math>f : \mathcal F \to X</math>.

Revision as of 17:04, 10 January 2019

A distribution of X over Y is any function f:YX.

Examples:

  • A probability distribution over a finite sample space is a distribution of probabilities [0,1] over the sample space Ω. In other words, a probability distribution is a function p:Ω[0,1]. In the case of probability distributions, we also require some extra conditions (namely that ωΩp(ω)=1).
  • A wealth distribution over people is a distribution of wealth (R) over a set of people (P), that is, a function w:PR, where P is a set of people. Thus, if xP then w(x) is x's wealth.
  • A gender distribution over people assigns a gender to each person. g:PG
  • A gender distribution can also be thought of as a distribution of sets of people over genders. g:G2P where g(x)=g1({x})={pP:g(p)=x}

The definition above runs into trouble when the set Y is uncountable. In this case, we might not be able to find any function that satisfies the extra conditions we want to place on the distribution (e.g. in the case of probability distributions, we want to assign 0 to every individual outcome, but then the sum is also 0 rather than 1).

It seems like one way to get around this is to change the type of a distribution. Namely, rather than a function f:YX we have some collection F2Y of subsets of Y, and we define a function f:FX.