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Beschreibung
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.
Details
| Verlag | Springer International Publishing |
| Ersterscheinung | 27. September 2020 |
| Maße | 23.5 cm x 15.5 cm |
| Gewicht | 160 Gramm |
| Format | Softcover |
| ISBN-13 | 9783030543037 |
| Auflage | 1st ed. 2020 |
| Seiten | 85 |
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