Hi Phil.
Yes, I have seen RandomDataImpl and the next{Int, Poisson, ...}, but
as you mention not that many distributions are supported. What I
talked about is kind of the nextInversionDeviate-idea.
I must admit, that I find it a bit weird that the distributions are
separated from the random generation, i.e. the next{Int, ...}-method.
Ideally, when you have an instance of a distribution with the
parameters already specified, it would be nice to just get a sample
from the distribution just like when you find probability mass or what
over.
Yes, ContinuousDistribution have it, but DiscretDistributions doesn't?
And discrete have inverse cdfs as well.
Cheers, Mikkel.
2009/10/27 Phil Steitz <phil.ste...@gmail.com>:
> Mikkel Meyer Andersen wrote:
>> Dear community.
>>
>> I've just started using the Apache Commons Math library. In regards to
>> generating random data from probability distributions, the library
>> doesn't support generating random data using the inverse cdf method
>> although a lot of the distributions gives the possibility to calculate
>> the inverse cdf.
>
> Have you looked at the RandomData interface and RandomDataImpl
> class? This class provides methods for generating random deviates
> from multiple distributions, in some cases using inverse cdfs (see
> e.g. RandomDataImpl#nextExponential().
>>
>> Are there any particular good reason for this?
>
>>
>> If not, I would create a public interface
>> DistributionWithInverseCumulativeProbability (who has a better name?)
>> with the method inverseCumulativeProbability (right now it's on
>> ContinuousDistribution and some other subclasses and don't seem to be
>> gathered in an interface) and all the distributions with the
>> inverseCumulativeProbability-method should implement this interface.
>
> I am not following you here. What exactly is the difference between
> DistributionWithInverseCumulativeProbability and
> ContinuousDistribution? ContinuousDistribution extends Distribution
> with the inverseCumulativeProbability method you are describing.
>>
>> With this small change, a new class called
>> RandomDistributionWithInverseCumulativeProbability (again, who has a
>> better name?) could simply use the uniform generator and a class
>> implementing DistributionWithInverseCumulativeProbability to generate
>> random data from that distribution.
>
> This is essentially what RandomDataImpl does for the distributions
> that it supports. Support for more distributions would be a welcome
> addition. I guess along the lines of what you are talking about
> above, it might make sense to add a single generic
> nextInversionDeviate method paramaterized by ContinuousDistribution.
> The implementation of this would use a uniform generator and
> inversion to generate deviates. That would be simpler than creating
> separate classes for each distribution or adding random data
> generation to the distributions themselves. It would also be more
> consistent with the current organization of the code, which locates
> random data generation in the random package.
>
> Phil
>>
>> What do you think about that idea? I look forward to comments,
>> suggestions, and preferably, better names :-).
>>
>> Cheers, Mikkel Meyer Andersen.
>>
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