Hopefully this isn't a duplicate, I've tried to search for similar things, but to no luck.

I'm curious on how you would computationally compute a random distribution of numbers that follows a bell curve that's got slightly different constraints than normal. For example, a 'regular' bell curve graph ranging from 0-100 would be generated by something like: (rand(100) + rand(100) + rand(100)) / 3

I would like to get some more information on how to 'customize' these graphs.

For example, let's say I want:

• Set min and max value. For this example, let's say 0-100
• Set more or less specific requirements. For example, I want the highest distribution to be 10-20, so those being the most common.
• then a 'normal' curve down to the edges, despite the off-balance. For example, there is the same chance of getting 0 and 100.
• Another possibility would be having a steeper slope on one side than the other, despite having a regular curve. (Regular distribution being 50, but a much greater chance of getting 0 than 100, for example)

I know I can get a steeper curve by increasing the number of rands that I get the average of. Would 'breaking' the average cause the imbalance I'm looking for there? For example, (rand(100) + rand(100) + rand(100) / 4) would cause a lower regular distribution.

Any real answers (with actual algorithms), explanations, or good references to articles count. (Please include a basic summary of the article for future reference. While I plan on reading through them, future people may come across a broken article, and that's really unfortunate )