Relation between the rate of convergence of strong law of large numbers   and the rate of concentration of Bayesian prior in game-theoretic probability
  We study the behavior of the capital process of a continuous Bayesian mixture of fixed proportion betting strategies in the one-sided unbounded forecasting game in game-theoretic probability. We establish the relation between the rate of convergence of the strong law of large numbers in the self-normalized form and the rate of divergence to infinity of the prior density around the origin. In particular we present prior densities ensuring the validity of Erdos-Feller-Kolmogorov-Petrowsky law of the iterated logarithm. 
Ryosuke Sato and Kenshi Miyabe and Akimichi Takemura
math.PR