Probabilty and Information Theory.; (Lecture Notes in Mathematics 89.)

On different characterizations of entropies.- The structure of capacity functions for compound channels.- Boolean algebraic methods in Markov chains.- Maxima of partial sums.- Series expansions for random processes.- Glivenko-Cantelli type theorems for distance functions based on the modified empirical distribution function of M. Kac and for the empirical process with random sample size in general.- On the continuity of Markov processes.- Some mathematical problems in statistical mechanics.- Asymptotic behaviour of the average probability of error for low rates of information transmission.- On the optimum rate of transmitting information.- A necessary and sufficient condition for the validity of the local ergodic theorem.- Recent results on mixing in topological measure spaces.- Convergence in probability and allied results.- Applications of almost surely convergent constructions of weakly convergent processes.- Random processes defined through the interaction of an infinite particle system.- The central limit theorem and ?-entropy.- Maximum probability estimators with a general loss function.