Comments on [math-692] have made me curious about how might we run into discrete distributions that do not extend AbstractIntegerDistribution in practical applications. Does anyone have the need for this? One reason that I am asking about this is that I have always felt a little funny about basically forcing sample spaces of probability spaces to be subsets of R by the setup we have. The way we are forced to remap values in IntegerDistribution is a little smelly and to actually compute anything for discrete distributions in the current setup, their sample spaces have to be mapped to some subset of the integers. I like the setup for classical continuous distributions over R and can live with it for discrete distributions over Z, which is all that we have ever implemented. To model the more general case, we would have to parameterize the type of the sample space. This may make sense and have value in the discrete case; but if done at the top of the hierarchy it would complicate things for the currently implemented distributions and force use of Double everywhere in place of double. If the only practical use case we can identify is discrete distributions over non-Integer domains, we could just create some kind of adapter. Any thoughts on this?
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