Choosing a reliable hypothesis
William Evans, Sridhar Rajagopalan, Umesh V. Vazirani · 1993
We study the problem of inferring an accurate model for a stochastic process from its output.We identify two desirable properties -resoluteness and reliability -of any identification algorithm.We prove that for any countable class of stochastic processes, there is an identification algorithm that has these properties.This result also formulates an optimization problem whose solution is sufficent to solve the identification problem.In this sense, our result provides an analogue to the Occam principle in a probabilistic setting.