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Let's say I want to sample from 100 Beta distributions, and in each successive one, the first parameter increases by 1. So given a current iteration number n, I want to make a list of

RandomVariate[ BetaDistribution[n, b] ]

for all n values from 1 to 100. Is there an easy way to do this in Mathematica? I assume I would be able to find a way without needing to define 100 different distributions.

Edit 2: I changed the distribution I was talking about slightly--I'm asking this as a general question, the actual distribution I'm trying to sample from is a little more complicated but I'm hoping that if there's a solution that works for this, I can apply that solution elsewhere.

Edit: I thought this was possible with a For loop but after trying it out looks like I was wrong. Any help is appreciated

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  • $\begingroup$ RandomVariate[NormalDistribution[0, sv], 100] + Range@100... $\endgroup$
    – ciao
    Commented Apr 1, 2020 at 6:54
  • $\begingroup$ @ciao Sorry, I'll edit my OP, I forgot that Normal distributions can just be shifted like that. What I'm asking is if there's a more general way to do this--I want to be able to apply this to, say, the standard deviation instead of the mean, or to another distribution altogether. My example was just a simple example of what I'm looking for. $\endgroup$
    – rarelyNecessary
    Commented Apr 1, 2020 at 7:08

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I found what I was looking for. The example that I gave can be done (if we want to do it in one line) with:

RandomVariate[#]&/@(BetaDistribution[#,b]&/@Range[100]);

If we wanted to do the same thing for both variables, we could do something instead like

RandomVariate[#]&/@(MapThread[BetaDistribution[#1,#2]&,{Range[100],Range[100]}]);

This seems pretty slow right now for what I'm using it for, so if anyone has any optimization suggestions let me know!

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  • $\begingroup$ RandomVariate[BetaDistribution[#, b], 1][[1]] & /@ Range[100] is about 6 times faster (although neither approach seems slow). $\endgroup$
    – JimB
    Commented Apr 1, 2020 at 15:00
  • $\begingroup$ @JimB Thanks! It looks like the slowness was because I was using CategoricalDistribution in my code where BernoulliDistribution was just as useful and also faster. $\endgroup$
    – rarelyNecessary
    Commented Apr 23, 2020 at 22:40

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