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Of you choose a random element from [1,2,3,4,5,6], they will have equal probability of 1/6 = 16.66%
Now, if you chose from [1,1,2,2,2,3,3,4,5,6], probabilities change to
1: 2/10 = 20%
2: 3/10 = 30%
3: 20%
4, 5 or 6: 10%
Now you need some reverse engineered maths to input avg and stddev, and produce the corresponding array. -
@gosubinit an easier task would be to concatenate [1,2,3,4,5,6] a thousand times into a single array and pick an element randomly, which should be better than rand()%6+1
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@Lensflare if I understood correctly, @donuts wants to be able to skew the results towards a given average and standard deviation, so I illustrated the concept.
I think your suggestion will produce equal probabilities for all results, right? -
@gosubinit yes, this was just a suggestion for a far easier solution :)
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@gosubinit I read afterwards it's like getting the inverse of a CDF function which kind of looks like what a hashing function for a Map usually does.
But then how do you create the right formula based on the parameters... I assume there so lib they already does since it's probably a common problem in the computing math/stats world so wondering if anyone knew existing solutions/libs
Math is hard.
When you wanted to know deep learning immediately
:)) finally a good answer
I want to run a simulation like rolling a dice N times and find out what's the distribution/probability of getting certain SUMs after the rolls.
However the dice is more likely to land on a lower number, and/or depends on what the number it landed on previously.
Stats wise I think ideally I want to be able to just input the avg, standard deviation, and skewness into a Random class constructor, and then ask it for N "random" numbers.
question
stats
math