On 19 Feb 2002 13:13:08 -0800, [EMAIL PROTECTED] (The Truth) wrote: > Glen Barnett <[EMAIL PROTECTED]> wrote in message >news:<[EMAIL PROTECTED]>...
> > The Truth wrote: > > > Are there any "Numerical Recipes" like textbook on statistics and probability ? > > > Just wondering.. > > > > What do you mean, a book with algorithms for statistics and probability > > or a handbook/cookbook list of techniques with some basic explanation? [ ... ] > > I suppose I should have been more clear with my question. What I > essentially require is a textbook which presents algorithms like Monte > Carlo, Principal Component Analysis, Clustering methods, > MANOVA/MANACOVA methods etc. and provides source code (in C , C++ or > Fortran) or pseudocode together with short explanations of the > algorithms. Unreasonable dreams. Maybe your university would have some trace of the original BMD package. I think it was in the public domain, and that it was the source of many routines for SPSS, SAS, and BMDP. But those were simple routines. That should have Principal components, and perhaps some clustering. However: There are dozens of Monte Carlo methods; you might find a book for whatever your particular *field* happens to be. And check the SPSS 'proximities' routines to get a notion of the complexity of a driver-routine for clustering. It has 3 dozen or so criteria for closeness. Clusters can use [some criterion] and apply it to each case as it relates to each other case, or to each previous group. Or it can relate created-groups to other created-groups. Then there is the big choice of whether cases start separate, to be joined; or start out joined, to become separate. Compared to Clustering, MANOVA might have some pretty good routines. But I have not seen much great program organization for MANOVA procedures, once you get beyond 'discriminant function' and the paradigm of multi-regression with multiple-criteria. 'Pseudocode,' however, is almost what it looks like when you start with matrix routines, in SPSS or R or various specialty programs. You do have to speak the language of matrices, so you know when it is, that you want to get an eigenvector; and what to do with it. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =================================================================
