[Numpy-discussion] Log Arrays

[Charles R Harris]

On Thu, May 8, 2008 at 11:46 AM, Anne Archibald <peridot.faceted at gmail.com>
wrote:

> 2008/5/8 Charles R Harris <charlesr.harris at gmail.com>:
> >
> > On Thu, May 8, 2008 at 10:56 AM, Robert Kern <robert.kern at gmail.com>
> wrote:
> > >
> > > When you're running an optimizer over a PDF, you will be stuck in the
> > > region of exp(-1000) for a substantial amount of time before you get
> > > to the peak. If you don't use the log representation, you will never
> > > get to the peak because all of the gradient information is lost to
> > > floating point error. You can consult any book on computational
> > > statistics for many more examples. This is a long-established best
> > > practice in statistics.
> >
> > But IEEE is already a log representation. You aren't gaining precision,
> you
> > are gaining more bits in the exponent at the expense of fewer bits in the
> > mantissa, i.e., less precision. As I say, it may be convenient, but if
> cpu
> > cycles matter, it isn't efficient.
>
> Efficiency is not the point here. IEEE floats simply cannot represent
> the difference between exp(-1000) and exp(-1001). This difference can
> matter in many contexts.
>
> For example, suppose I have observed a source in X-rays and I want to
> fit a blackbody spectrum. I have, say, hundreds of spectral bins, with
> a number of photons in each. For any temperature T and amplitude A, I
> can compute the distribution of photons in each bin (Poisson with mean
> depending on T and A), and obtain the probability of obtaining the
> observed number of photons in each bin. Even if a blackbody with
> temperature T and amplitude A is a perfect fit I should expect this


This is an interesting problem, partly because I was one of the first guys
to use synthetic spectra (NO, OH), to fit temperatures to spectral data. But
also because it might look like the Poisson matters. But probably not, the
curve fitting effectively aggregates data and the central limit theorem
kicks in, so that the normal least squares approach will probably work.
Also, if the number of photons in a bin is much more that about 10, the
computation of the Poisson distribution probably uses a Gaussian, with
perhaps a small adjustment for the tail. So I'm curious, did you find any
significant difference trying both methods?

Chuck
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