Probability and Information Theory
Probability and Information Theory Proceedings of the International Symposium at McMaster University, Canada, April, 1968 / [electronic resource] :
edited by M. Behara, K. Krickeberg, J. Wolfowitz.
- IV, 260 p. online resource.
- Lecture Notes in Mathematics, 89 0075-8434 ; .
- 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.
9783540360988
10.1007/BFb0079113 doi
Distribution (Probability theory.
Computer science.
Science (General).
Probability Theory and Stochastic Processes.
Probability and Statistics in Computer Science.
Popular Science, general.
QA273.A1-274.9 QA274-274.9
519.2
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.
9783540360988
10.1007/BFb0079113 doi
Distribution (Probability theory.
Computer science.
Science (General).
Probability Theory and Stochastic Processes.
Probability and Statistics in Computer Science.
Popular Science, general.
QA273.A1-274.9 QA274-274.9
519.2