Stationary lower probabilities and unstable averages

Abstract

This paper explores the possibilities for probability-like models of stationary nondeterministic phenomena that possess divergent but bounded time averages. A random sequence described by a stationary probability measure must have almost surely convergent time averages whenever it has almost surely bounded time averages. Hence, no measure can provide the mathematical model we desire. In turning to lower probability based models we first explore the relationships between divergence, stationarity, and monotone continuity and those between monotone continuity and unicity of extensions. We then construct several examples of stationary lower probabilities for sequences of uniformly bounded random variables such that divergence of time averages occurs with lower probability one. We conclude with some remarks on the problem of estimating lower probability models on the basis of cylinder set observations.