Hilberg Exponents: New Measures of Long Memory in the Process

The paper concerns the rates of hyperbolic growth of mutual information computed for a stationary measure or for a universal code. The rates are called Hilberg exponents and four such quantities are defined for each measure and each code: two random exponents and two expected exponents. A particularly interesting case arises for conditional algorithmic mutual information. In this case, the random Hilberg exponents are almost surely constant on ergodic sources and are bounded by the expected Hilberg exponents. This property is a “second-order” analogue of the Shannon-McMillan-Breiman theorem, proved without invoking the ergodic theorem. It carries over to Hilberg exponents for the underlying probability measure via Shannon-Fano coding and Barron inequality. Moreover, the expected Hilberg exponents can be linked for different universal codes. Namely, if one code dominates another, the expected Hilberg exponents are greater for the former than for the latter. The paper is concluded by an evaluation of Hilberg exponents for certain sources such as the Bayesian Bernoulli process and the Santa Fe processes.