[Numpy-discussion] Log Arrays
Thu May 8 12:05:48 CDT 2008
On Thu, May 8, 2008 at 12:02 PM, Charles R Harris
> On Thu, May 8, 2008 at 10:56 AM, Robert Kern <firstname.lastname@example.org> wrote:
>> On Thu, May 8, 2008 at 11:25 AM, Charles R Harris
>> <email@example.com> wrote:
>> > On Thu, May 8, 2008 at 10:11 AM, Anne Archibald
>> > <firstname.lastname@example.org>
>> > wrote:
>> >> 2008/5/8 Charles R Harris <email@example.com>:
>> >> >
>> >> > What realistic probability is in the range exp(-1000) ?
>> >> Well, I ran into it while doing a maximum-likelihood fit - my early
>> >> guesses had exceedingly low probabilities, but I needed to know which
>> >> way the probabilities were increasing.
>> > The number of bosons in the universe is only on the order of 1e-42.
>> > Exp(-1000) may be convenient, but as a probability it is a delusion. The
>> > hypothesis "none of the above" would have a much larger prior.
>> 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.
*YES*. As David pointed out, many of these PDFs are in exponential
form. Most of the meaningful variation is in the exponent, not the
"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco
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