1st Edition 2020
Paperback (27 Sep 2020)
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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 |
| Publisher: | Springer International Publishing |
| Imprint: | Springer |
| Pub date: | 27 Sep 2020 |
| Edition: | 1st Edition 2020 |
| Language: | English |
| Number of pages: | 85 |
| Weight: | 145g |
| Height: | 235mm |
| Width: | 155mm |
| Spine width: | 5mm |
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