Comments on: Probability function names

By: John

John — Wed, 04 Jul 2012 05:10:36 +0000

Tom: I've written a class library in C++ and one in C#. Both use distribution objects with member functions for CDF etc. For example, the NormalDistribution class has methods PDF, LogPDF, CDF, CDFInverse, CCDF, CCDFInverse, Mean, Variance, RandomValue, etc. One advantage to this approach is that you can write generic code that takes a distribution class. For example, a unit test might integrate the PDF function and compare the result to the CDF function. If all the distribution classes have a common base class or implement a common abstract interface, you could write this test once and pass it objects representing several different distribution families.

By: Tom Nish

Tom Nish — Wed, 04 Jul 2012 00:51:25 +0000

Dr. Cook,
How would you name probability function if you were writing such a library from scratch? I ask b/c I’m doing something similar for Perl, built upon GSL’s randist functions. GSL names their sampler, pdf, cdf, ccdf and quantile functions gsl_ran_gaussian, gsl_ran_gaussian_pdf and gsl_ran_gaussian_cdf_P, gsl_ran_gaussian_cdf_Q, gsl_ran_gaussian_cdf_Pinv, and gsl_ran_gaussian_cdf_Qinv, which is quite verbose though consistent.

Right now, I’m thinking of including a straight GSL-binding, but also provide alternative interfaces mimicking other langauges (like R and octave/matlab, by wrapping gsl_ran_gaussian into rnorm for R and normrnd for octave).
-Tom

By: Rick Wicklin

Rick Wicklin — Tue, 03 Jul 2012 14:19:22 +0000

In SAS the names are CDF and SDF. Therea re also separate LOGCDF and LOGSDF functions, which are sometimes more efficient or accurate than computing the distribution and than taking the logarithm. The other esential functions for dealing with distributions are PDF, QUANTILE, and RAND. For details and a discussion that compares these naming scheme with other languages, see "Four essential functions for statistical programmers"

By: Benoit Hamelin

Benoit Hamelin — Tue, 03 Jul 2012 12:21:43 +0000

Matlab uses a scheme similar to R, where probability functions go {family}{function type}. So, one has normcdf for the CDF of the normal function, and tcdf for that of the Student t distribution, and so on. Surprisingly, a quick look through the documentation of the probability toolbox could not unearth any obvious equivalent for the CCDF, which seems like an unfortunate omission. It does have, however, inverse-CDF functions, such as norminv and tinv: useful for statistical estimation and hypothesis testing, but confusing when looking for CCDFs.