I'm not sure what you are quoting with your first line, but, of course, there isn't a single inverse for any distribution. One task in statistics is hypothesis testing. In traditional statistics to do this you require the inverse cumulative distribution. Its well and consistently defined for every single dimensional distribution. It is also used in setting confidence limits and many other purposes. A numerical statistical package that doesn't include it is worthless. We also use the inverse cumulative in setting confidence bounds. Roughly speaking -- given that the underlying process is controlled by the following distribution, what x[lb] and x[ub] can I be 95% certain that any specific single sample will lie between? (A "single sample" actually could be a particular, single statistic for a set of samples, you just need the right distribution). The inverse of the complementary CDF is also useful but trivially derived from the other. Nice to have both, but not strictly necessary. *Some* distributions, such as the negative binomial distribution (note, NOT a function) and the binomial distribution are discrete distribution. It then is also meaningful to define an inverse for the PDF, especially a "fuzzy" inverse (how long a string of failures has a probability of 0.05 of occurring). Of course, in that case the inverse is not generally a function. Once in a great while, this might be useful. We can also take the distributional parameters and stop treating them like indexes for the family of CDFs and treat them like function arguments. We can then speak meaningfully about inverses for each of them. So, given the CDF for the normal distribution we have, lets say (this is math not any proposal for C++ naming): CDFz[mu, sigma](x) -> P becomes CDFz(x, mu, sigma) -> P The "standard" inverse CDF is then CDF'z(p, mu, sigma) -> x And one of the others is: CDF'z(x, mu, p) -> sigma I.e., given that I know a sample was generated from the normal distribution with mean mu and that the probability that the sample was greater than a particular precise value, x, is a particular precise probability, p, then what is the standard deviation, sigma, for that distribution? This is an important question algebraically. It allows us to derive distributions for parameter estimation that we can then use the inverse cumulative distribution function to give us confidence bounds for parameters. For example, given a particular sample drawn from say, a chi-square distribution, what is the distribution of possible values for the number of degrees of freedom? There may be situations where a particular distribution applies where a numerical inversion around a parameter is called for, but I can't think of any. Can you give me a reasonable scenario where these inverses around the parameters would be widely used? Lets have a use-case. I certainly think that after the common structure of the distribution classes have been put in place it is reasonable to ask what additional, distribution specific, methods should be added. If you want to put every formula in the handbooks in, go ahead -- little of it will ever be used in practice, but it will be there if some unanticipated need comes up and the user will be able to avoid the bother of looking up the formula themself. Some kind of naming convention for some of this distribution specific stuff seems reasonable. Having read accessors for each distribution parameter seems like a good idea, for example ("(x - aNormDist.mu)/aNormDist.sigma" where, in this case aNormDist.mu = aNormDist.mean and aNormDist.sigma = aNormDist.standardDeviation). Topher At 05:05 AM 7/14/2006, you wrote:
THE inverse?
Another quick question - I'm still in partial disambiguation mode.
With the negative binomial distribution function (or are there more than one but one is THE Standard one?), which is **THE** inverse?
the one that tells you the number of failures (MathCAD qnbinom & DCDFLIB)
or the one that tells you the success probability? (Cephes, Wikipedia & DCDFLIB)
John's response to this question was faintly blasphemous ;-)
Same question with F and chisqr of course...
Both/all of course are potentially useful :-)
(and I feel all should be provided).
Paul
--- Paul A Bristow Prizet Farmhouse, Kendal, Cumbria UK LA8 8AB +44 1539561830 & SMS, Mobile +44 7714 330204 & SMS pbristow@hetp.u-net.com
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