At 10:45 AM 7/14/2006, Paul Bristow wrote:
So how to we find out what is considered "standard" - ask you? consult Mathemetica's documentation?textbooks..? Is there agreement on standard? I suspect so, but
You'll find a lot of variation in how the distribution parameters are expressed for some distributions but all single-dimensional distribution families are pretty unambiguous on this point. There are some number of parameters that indexes a specific distribution from a family of distributions. Random variables are associated with that distribution. There is a quantity, "x" representing possible values for such a random variable. The integral of the PDF of x (or sum for a discrete variate/distribution) from -infinity to t is the CDF for that distribution at t. It is the probability that a random variabe will have a value less than or equal to t. The inverse CDF sometimes called the "quantile" in statistical packages (a usage taken from statistics in the social sciences) is the functional inverse of the CDF function. It's value for a particular "p" is the value for t with a probability p that a random variable will be less than it. I don't think you'll find any real disagreement in any source about this. I've finally figured out that you guys are not really talking about functional inverses at all. You're saying "inverse" when you mean a parameter estimator. As I posted a little while ago, that's a much more elaborate issue than you think it is. Topher