Probability and information theory : proceedings of the International symposium at McMaster University, Canada, April, 1968
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| Tác giả của công ty: | |
|---|---|
| Tác giả khác: | , , |
| Định dạng: | Livre numérique |
| Ngôn ngữ: | Anglais |
| Được phát hành: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Loạt: | Lecture notes in mathematics
89 |
| Những chủ đề: | |
| Truy cập trực tuyến: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Chú thích: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Probability and information theory, proceedings of the International symposium at McMaster University, Canada, April, 1968, edited by M. Behara, K. Krickeberg, and J. Wolfowitz, Berlin, Springer-Verlag, 1969, 1 vol. (256 p.), Lecture notes in mathematics, 0-387-04608-9 • Probability and Information Theory, Texte imprimé, 9783662180693 |
Mục lục:
- 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.