- --On Monday, May 07, 2001, 11:56 AM -0700 Rina Dechter
<dechter@ics.uci.edu> wrote:
> The question of evaluating the quality of an approximation to
> probabilistic inference is really a very important one and a difficult
> one, with which we also struggle with in Irvine.
>
> Sometimes it is possible to generate by an approximation an upper and
> lower bounds of the exact probability which can bound provide some
> estimate of the accuracy.
> However, such methods are often very inaccurate.
> Therefore one produces arbitrary approximations (no guarantees).
> In that case, I dont see any other way but to test your approximation
> algorithm on relatively small or moderate networks and compare against
> the exact figure (computed by an exact algorithm) with the
> hope that the information gained from such comparison scales up.
> You can then try too show superiority relative to other
> competing approximation algorithms.
I agree with Rina here. I suggest that you find some networks that are
large or complex enough for your algorithm to be challenging and yet
solvable using exact methods. This is the approach that my doctoral
student, Jian Cheng, and I followed when testing stochastic sampling
algorithms (e.g., http://www.jair.org/abstracts/cheng00a.html). Unless you
have a good theory for putting bounds on your posteriors (we have a
forthcoming UAI paper along these lines; still we test this approach by
comparing the results to exact answers!), only when you have exact answers
can you saying anything meaningful about your approximate results.
> I have seen some people comparing approximation reults to each other
> (comparing to Gibbs sampling for instance) however such comparisons are
> meaningless, I think.
I agree here.
Marek
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Marek J. Druzdzel http://www.pitt.edu/~druzdzel
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