论文检索

SUMMARY It is argued that all the useful information in observed data that can be extracted with a selected class of modeled distributions, will be obtained if we calculate the stochastic complexity, defined to be the shortest description length of the data. The same quantity also determines the greatest lower bound for prediction errors when the data are sequentially predicted. An abstract definition of stochastic complexity is given along with two fundamental theorems…

随机复杂度与建模AI 解读

As a modification of the notion of algorithmic complexity, the stochastic complexity of a string of data, relative to a class of probabilistic models, is defined to be the fewest number of binary digits with which the data can be encoded by taking advantage of the selected models. The computation of the stochastic complexity produces a model, which may be taken to incorporate all the statistical information in the data that can be extracted with the chosen model class. This…