Re: Simple C Statistics

From: Ross A. Finlayson (raf_at_tiki-lounge.com)
Date: 03/06/05 

Date: 5 Mar 2005 19:47:26 -0800

beliavsky@aol.com wrote:
> Ross A. Finlayson wrote:
> > Hi,
> >
> > I wonder if there is a simple library or set of functions for basic
> > statistical functions, like what may be on a pocket calculator,
> without
> > getting into linking into the language runtimes of the S or R
> > languages.
>
> The GNU Scientific Library has C code for statistics, see
> http://www.gnu.org/software/gsl/manual/gsl-ref_toc.html , as does the
> commercial Numerical Recipes in C, see http://www.nr.com . Gretl at
> http://gretl.sourceforge.net/ is an open source statistical package
> written in C . Statcodes at http://www.astro.psu.edu/statcodes/ has
> links to public domain Fortran and C code for statistics.

Hi Beliavsky,

Thank you. That is mind-boggling.

Basically I figure there is a sample, the sample space, the population,
which might be the same thing as the sample space, the probability of a
sample being selected at "random" for various distributions of metrics
of the samples of the population, which might be unknown or
parameterized as opposed to knowing every element of the sample space
in advance in which case it is fixed, the probability, P, of the binary
predicate, into fuzzy and multivalent metrical predicates, these are
words I string together without qualification.

That's a problem in "statistics", those web pages appear immaculately
respectable, but the abuse in statistics is of the metrics and sample
spaces from the actual population.

typedef void* sample_t;

I'm way back on regression, ANOVA, ANCOVA, MANOVA, MANCOVA,
curve-fitting, best-guess estimates for algorithm selection on
instrumented algorithm conditions and contraints, and other nonsense
drooled onto the keyboard, I only had three or four years of college
stat.

That's about statistics, about mathematics and logic I'm an absolutist.

I think the Bayesian methods are very useful for decision making based
on essentially neural networks, as neural networks are modelled in some
sense Bayesianly.

The neural net (keywords forward, backward, and mixed mode propagation,
transfer and decision function, Kohenen, Hopfield, Markov, binary and
analog weights, graph, network and circuit theories, which are the same
thing) is also a very useful tool, and, the implementations are not
necessarily minimal but they are not necessarily massive.

So, my next question is "what C libraries are available for portable
neural net implementations", and an answer may well be "go find it
yourself."

http://www.google.com/search?q=Bayesianly+neural

That is mildly disturbing.

Warm regards,

Ross F.

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