Wikipedia:WikiProject Probability/Probability distribution article structure

The printable version is no longer supported and may have rendering errors. Please update your browser bookmarks and please use the default browser print function instead.

Probability distribution articles are generally divided into one of several subcategories: for example Category:Discrete distributions and Category:Continuous distributions. The prototypical continuous distribution is the Exponential distribution and the prototypical discrete distribution is the Poisson distribution.

The following is a list of suggested standards for a probability distribution article.

Standard usage

In-line math - if done with <math> tags, should be done in such a way that PNG rendering is NOT forced. "Displayed" (i.e. non-inline) math should be done with <math> tags and should be done in such a way that PNG rendering IS forced. In some cases (e.g. fractions) it is naturally forced, but if it is not naturally forced it can be made so by appending a "\," (space) at the end of the equation. This is so that those who set their math preferences to "Always render PNG" will get as much PNG as possible, while those who prefer "HTML when possible or else PNG" will get as much HTML as possible. Those who prefer "HTML if very simple, else PNG" will get HTML inline, PNG for non-inline. Whether inline math should use <math> tags is a subject of discussion at Wikipedia:Manual of Style (mathematics).

Standard layout

Title - The title of each article will be: "XXX distribution" where "XXX" is the name of the distribution.

Template - Each article will contain a template summarizing some important aspects of the probability distribution. This template is described in Template:Probability distribution and the talk page Template talk:Probability distribution describes the standards for this template as well as the status of each article as regards this template.

Specification of the distribution - A continuous distribution is described by a CDF (cumulative distribution function) and, when possible, a PDF (probability density function) which is the derivative of the CDF. A discrete distribution is described by a CDF (cumulative distribution function) and the associated PMF (probability mass function). Each function may also be a function of any number of parameters.
- For the continuous distribution, the PDF will be written as $f(x;a,b,c,\dots )$ where x is the value of the random variate (a real number), and a,b,c... are the parameter values, when they exist. The CDF is written $F(x;a,b,c,\dots )$ .
- For the discrete distribution, the PMF will be written as $f(k;a,b,c,\dots )$ where $k$ is the value of the random variate (an integer or perhaps a rational number), and $a,b,c,\dots$ are the parameter values, when they exist. The CDF is written $F(k;a,b,c,\dots )$ .