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POBest way to generate a set of integers of size N, distributed like a normal distribution, given a mean and std. deviation
 

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  1. POBest way to generate a set of integers of size N, distributed like a normal distribution, given a mean and std. deviation
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    <p>I'm looking for a way to generate a set of integers with a specified mean and std. deviation.</p> <p>Using the random library, it is possible to generate a set of random doubles distributed in gaussian fashion, this would look something like this:</p> <pre><code>#include &lt;tr1/random&gt; std::tr1::normal_distribution&lt;double&gt; normal(mean, stdDev); std::tr1::ranlux64_base_01 eng; eng.seed(1000); for (int i = 0; i &lt; N; i++) { gaussiannums[i] = normal(eng); } </code></pre> <p>However, for my application, I need integers instead of doubles. So my question is, how would you generate the equivalent of the above but for integers instead of doubles? One possible path to take is to convert the doubles into integers in some fashion, but I don't know enough about how the random library works to know whether this can be done in a fashion that really preserves the bell shape and the mean/std. deviation.</p> <p>I should mention that the goal here is not so much randomness, as it is to get a set of integers of a specific size, with the correct mean and std. deviation.</p> <p>Ideally I would also like to specify the minimum and maximum values that can be produced, but I have not found any way to do this even for doubles, so any suggestions on this are also welcome.</p>
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      1. POBest way to generate a set of integers of size N, distributed like a normal distribution, given a mean and std. deviation
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    1. COYou did specify the minimum and maximum values... when you said "Gaussian". You either need to specify what your goals are (what you need randomness for), instead of your approach (generating "gaussian" integers), or go back and study statistics so you understand why your question makes no sense. (Among other things, to generate integers, you need a discrete distribution, but gaussian is continuous).
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      1. POBest way to generate a set of integers of size N, distributed like a normal distribution, given a mean and std. deviation
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    2. COI somewhat see your point, but I still think it makes sense. What I want to do is to generate a set of integers that are distributed over a range, satisfying a given mean and std. dev. That is the most important part, but I would also like these integers to form a bell curve, although it cannot be strictly gaussian, I would like it to preserve this property as much as possible. Maybe rounding is actually just the way then?
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      1. POBest way to generate a set of integers of size N, distributed like a normal distribution, given a mean and std. deviation
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    3. COThen I suggest you compute a gaussian CDF across the range of integers you're interested in, stretch it slightly to account for the missing tails, and store it in an array. Then you generate uniform reals in the range [0:1], and use binary search to invert the CDF. This procedure for generating samples from an arbitrary CDF is pretty standard.
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      1. POBest way to generate a set of integers of size N, distributed like a normal distribution, given a mean and std. deviation
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