Interesting for its sociopolitical primary application, but this paragraph from the discussion section shows its general relevance to a lot of strong-AI tasks:
It applies whenever a
‘‘bell-ringer’’ event must be found by sampling with replacement,
but can be recognized when seen. For example, one can thus sample
paths through a trellis or hidden Markov model when their number
is too large to enumerate explicitly, but one path can be recognized
(e.g., by secondary testing) as the desired bell ringer. It seems
peculiar that the method is not better known.
It does seem a little peculiar, although it's not quite as unknown as the author implies; rather, it provides a mathematical justification for one of the hacks we would sometimes try (often helpfully!) if our figure-of-merit was being overly dominated by high-ranking items: take the square root of the probability[0] and use that. :)
[0] Well, once we'd got to the point of this kind of hackery to improve our performance, it typically wasn't much of a probability anymore, at least not as such; call it a "probability-derived score".
It applies whenever a ‘‘bell-ringer’’ event must be found by sampling with replacement, but can be recognized when seen. For example, one can thus sample paths through a trellis or hidden Markov model when their number is too large to enumerate explicitly, but one path can be recognized (e.g., by secondary testing) as the desired bell ringer. It seems peculiar that the method is not better known.
It does seem a little peculiar, although it's not quite as unknown as the author implies; rather, it provides a mathematical justification for one of the hacks we would sometimes try (often helpfully!) if our figure-of-merit was being overly dominated by high-ranking items: take the square root of the probability[0] and use that. :)
[0] Well, once we'd got to the point of this kind of hackery to improve our performance, it typically wasn't much of a probability anymore, at least not as such; call it a "probability-derived score".