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 |
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.
Universal Time-Series Forecasting with Mixture Predictors features in the following genres: Mathematical theory of computation, Artificial intelligence, Maths for computer scientists
Universal Time-Series Forecasting with Mixture Predictors is available in Paperback, Ebook
Universal Time-Series Forecasting with Mixture Predictors was written by Daniil Ryabko and published by Springer Nature Switzerland AG
Universal Time-Series Forecasting with Mixture Predictors has 85 pages
Yes it is part of SpringerBriefs in Computer Science series
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