Separation of variables for Named Probability Density Distributions

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Discussion Overview

The discussion revolves around the conditions under which a joint probability density distribution can be expressed as a product of individual probability density functions for named distributions. Participants explore the implications of this factorization in the context of probability theory.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant inquires about the conditions under which a joint probability density distribution can be expressed as a product of individual distributions.
  • Another participant questions whether the equality is determined by the distribution itself or by properties of the vector ## \vec{x} ##.
  • A participant provides a resource listing named distributions and mentions that their list includes distributions limited to the exponential term.
  • There is a repeated inquiry about the necessary and sufficient conditions for the factorization of the joint distribution.
  • One participant asserts that independence is a necessary condition for the factorization to hold.

Areas of Agreement / Disagreement

Participants express uncertainty regarding the conditions for the factorization, with some suggesting independence as a necessary condition, while others explore different perspectives on the role of the distributions and properties of ## \vec{x} ##. No consensus is reached.

Contextual Notes

The discussion does not clarify the specific assumptions or definitions that might influence the factorization of the distributions, nor does it resolve the mathematical implications of the proposed conditions.

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TL;DR
What are the named Probability Density Distributions for which separation of variables can be performed?
Given a probability density distribution ##P(\vec{x})##, for what named distributions is the following true:
\begin{equation}
\begin{split}
P(\vec{x}) &= P_1(x_1) P_2(x_2) ... P_n(x_n)
\end{split}
\end{equation}
 
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Do you think that that equality is determined by the distribution or by some property of ## \vec{x} ##?

Do you have a complete list of 'named distributions'?
 
So what condition is necessary and sufficient for ## P(x_1, x_2, ... ,x_n) = P_1(x_1) P_2(x_2) ... P_n(x_n) ##?
 
pbuk said:
So what condition is necessary and sufficient for ## P(x_1, x_2, ... ,x_n) = P_1(x_1) P_2(x_2) ... P_n(x_n) ##?
Independence
 
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