Re: Generalised bisection

On Fri, Mar 13, 2009 at 3:19 PM, Steven Tweed [off-list ref] wrote:

Underflow when using probabilities and lack of precision (rather than
overflow) when using negated logarithms are well known problems in the
kind of probabilistic object tracking, inference in graphical networks
and object identification processes I work with (in computer vision).
I there may well be other areas of Bayesian decision theory where this
doesn't happen, and indeed a _very_ quick scan through your document
suggests that you're adding to tallying information on each timestep
and recalcuating the entire model from those tallys, which is one of
the few cases where you can't really do rescaling. I'll try and have a
more detailled read over the weekend.

That is useful information, thanks.

It is not obvious how to perform this algorithm incrementally, because
of the need to
marginalise out the fault rate. As I understand it, marginalisation
has to be done after you
have incorporated all your information into the model, which means we
can't use the
usual bayesian updating.

On Fri, Mar 13, 2009 at 12:49 PM, Ealdwulf Wuffinga
[off-list ref] wrote:

quoted

One issue in BBChop which should be easy to fix, is that I use a dumb
way of calculating Beta functions. These
are ratios of factorials, so the subexpressions get stupidly big very
quickly. But I don't think that is the only problem.

Yes, "Numerical Recipes" seems to suggest that computing with
log-factorials and exponentiating works reasonably, although I've
never tried it and NR does occasionally get things completely wrong...

I have implemented this and it does indeed allow the program to work
in more cases
without underflow, with ordinary floating point. However, I think the
underflow can still occur
in plausible use cases.

The problem is still the Beta function. In bbchop it is always passed
D and T where D is
the sum of the number of detecting observations in some of the
revisions, and T is the
same for nondetecting observations. Beta(x,y) underflows a python float
if both x and y are > ~550, and also in other cases when one is
smaller and the other,
larger. BBChop never looks again at a revision if the bug has been
observed there, but if
there are a large number of revisions, it might look at enough of them
to cause a problem.

Obviously no-one is going to manually do hundreds of observations, but
 I want BBChop
to work in the case where someone runs it on a machine in the corner
for a few days,
or even weeks,  to track down a bug which occurs too infrequently to
bisect manually.

Which means I'm still stuck with mpmath, or some equivalent.

Ealdwulf