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dices changes
tomason2 wrote
at 8:04 AM, Thursday April 5, 2007 EDT
now we can roll from 1 to 6 on dice - change it - from 1 to ..., how do u think?

Replies 1 - 7 of 7
fuzzycat wrote
at 9:21 AM, Thursday April 5, 2007 EDT
prob table, with 1-sided dice
def/att 2 3 4 5 6 7 8
1 100.0 100.0 100.0 100.0 100.0 100.0 100.0
2 0.0 100.0 100.0 100.0 100.0 100.0 100.0
3 0.0 0.0 100.0 100.0 100.0 100.0 100.0
4 0.0 0.0 0.0 100.0 100.0 100.0 100.0
5 0.0 0.0 0.0 0.0 100.0 100.0 100.0
6 0.0 0.0 0.0 0.0 0.0 100.0 100.0
7 0.0 0.0 0.0 0.0 0.0 0.0 100.0
8 0.0 0.0 0.0 0.0 0.0 0.0 0.0
fuzzycat wrote
at 9:35 AM, Thursday April 5, 2007 EDT
that doesnt look nice:

look i calculated for you the propabilities page for various n-sided dice.

http://kdice.wikispaces.com/prob+variances

You will see that exect of "1-sided dice" it does not make much difference for kdice!

e.g. the table for 6-sided dice and 18-sided dice are pretty much the same.
fuzzycat wrote
at 9:44 AM, Thursday April 5, 2007 EDT
I wonder how the the table for "infinite sided" dices would look.

However I'm not smart enough to properly insert lim n->inifite into that equations...
skrumgaer wrote
at 6:17 PM, Thursday April 5, 2007 EDT
Is a one-sided die a mobius?
skrumgaer wrote
at 6:20 PM, Thursday April 5, 2007 EDT
Speaking of mobii... How many colors are needed to color a map on a mobius?
JKD wrote
at 6:24 PM, Thursday April 5, 2007 EDT
You need to try max 2 (or 1) dice per land before trying 1-sided die ;)
Sock Puppet 100 wrote
at 5:16 AM, Friday April 6, 2007 EDT
(Maths geekery warning)

For an infinite sided dice you'd have to put a non-uniform probability distribution (like Poisson) on the numbers, and different distributions would give different results.

Another alternative would be to use 'dice' which give a real number uniformly distributed between 0 and 1.

It's not that surprising that the probabilities are similar for different sizes of dice... by the central limit theorem you can approximate the result of adding lots of independent random variables (like dice throws) by a normal distribution, and this approximation gets better the more dice you'd throw at once.
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