β Scribed by Khalil Sima'an
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Bayesian belief networks provide a natural, efficient method for representing probabilistic dependencies among a set of variables. For these reasons, numerous researchers are exploring the use of belief networks as a knowledge representation m artificial intelligence. Algorithms have been developed
We consider a measure ~b of computational complexity. The measure 9 determines a binary relation on the recursive functions; F is no harder to compute than G iff for every index g of G there is an index f off such that for nearly all x, the difficulty off at x (as measured by ~) is no more than the
We study the probabilistic (E, b)-complexity for linear problems equipped with Gaussian measures. The probabilistic (E, S)-complexity, comp@'(e, 6), is understood as the minimal cost required to compute approximations with error at most e on a set of measure at least 1 -6. We find estimates of comp@