The author considers the problem of sequential probability forecasting in the most general setting, where the observed data may exhibit an arbitrary form of stochastic dependence. All the results presented are theoretical, but they concern the foundations of some problems in such applied areas as machine learning, information theory and data compression.
| ISBN: | 9783030543037 |
| Publication date: | 27th September 2020 |
| Author: | Daniil Ryabko |
| Publisher: | Springer Nature Switzerland AG |
| Format: | Paperback |
| Pagination: | 85 pages |
| Series: | SpringerBriefs in Computer Science |
| Genres: |
Mathematical theory of computation Artificial intelligence Maths for computer scientists |