I see your point, Phil, and agree with you. Thanks for clarifying. Cheers, Mikkel. Den 01/11/2011 18.24 skrev "Phil Steitz" <phil.ste...@gmail.com>:
> On 11/1/11 1:05 AM, Mikkel Meyer Andersen wrote: > > 2011/10/30 Phil Steitz <phil.ste...@gmail.com>: > >> On 10/29/11 10:20 AM, cwinter wrote: > >>> Phil Steitz wrote: > >>>> I would say pull DiscreteDistribution out. That is where the > >>>> difference really lies. I have thought about suggesting that we > >>>> eliminate it altogether; but I still think there may be value in > >>>> supporting discrete distributions over sample spaces that are not > >>>> embedded in the integers. > >>>> > >>>> Phil > >>>> > >>> Empirical distributions are discrete by nature. Depending on the > underlying > >>> data, the domain is usually (a subset of) the reals or the integers. > >>> However, after moving probability(double) to Distribution, > >>> DiscreteDistribution will be an empty interface. Thus there is in fact > the > >>> question whether it should be eliminated. Otherwise it would be just a > >>> "flag" for discrete distributions and that's indeed independent of the > >>> sample space. > >> Maybe it would be best to eliminate IntegerDistribution then and > >> merge Distribution and ContinuousDistribution, so we have two roots > >> - DiscreteDistribution and ContinuousDistribution. The only reason > >> really to have DiscreteDistribution is if we want to support > >> distributions of RVs over sample spaces that are not subsets of Z. > >> There does not seem to be much enthusiasm for that (i.e. > >> parameterizing the type of the domain of the distribution and pmf), > >> so we might as well just collapse Discrete and Integer. Once you > >> pull out Discrete/Integer, there is not much value any more in > >> Distribution as a parent, so why not just drop both > >> IntegerDistribution and Distribution and move to two roots with > >> doubles / ints as domains and contracts cleaned up to deal with > >> discrete vs continuous cases consistently. > > If we have these two roots, I would propose an Distribution interface > > with e.g. with cdf and inverse cdf. Alternatively, an abstract class > > implementing a default solver for the inverse cdf. We might be able to > > make this generic parameterising the argument to cdf and others. > > The only way for this to work is to parameterize the type of the > sample space, which will then force Double to be used for the > continuous case. Why is it so bad to have two roots? > > What exactly do we gain by having the common parent? The inverse > cdf machinery will work only for the continuous (by that, I mean > real-valued RV) case. Note how it is overriden now in > AbstractIntegerDistribution. So why not just leave that alone and > separate out the discrete/integer/whatever-we-want-to-call-it case? > Discrete distributions are fundamentally different. They have > pmfs. They have discrete value sets. Inversion works differently. > Inequalities work differently. Why not just cleanly separate? > > Phil > > > > In my opinion, we benefit from having one such common ancestor instead > > of two "independent" linages. > > > >> Phil > >>> Christian > >>> > >>> -- > >>> View this message in context: > http://apache-commons.680414.n4.nabble.com/math-Distributions-over-sample-spaces-other-than-R-tp3931349p3951273.html > >>> Sent from the Commons - Dev mailing list archive at Nabble.com. > >>> > > --------------------------------------------------------------------- > > To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org > > For additional commands, e-mail: dev-h...@commons.apache.org > > > > > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org > For additional commands, e-mail: dev-h...@commons.apache.org > >
