Overview
- Considers problem of sequential probability forecasting in the most general setting
- Results presented concern the foundations of problems in areas such as machine learning, information theory and data compression
- Material presented in a way that assumes familiarity with basic concepts of probability and statistics
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (8 chapters)
Keywords
About this book
Reviews
“The author lists some open problems in extending the subject matter discussed in the book. … The book … should be of interest for those researchers interested in the study of problems of sequential prediction.” (B. L. S. Prakasa Rao, zbMATH 1479.62002, 2022)
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Universal Time-Series Forecasting with Mixture Predictors
Authors: Daniil Ryabko
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-030-54304-4
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Softcover ISBN: 978-3-030-54303-7Published: 27 September 2020
eBook ISBN: 978-3-030-54304-4Published: 26 September 2020
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: VIII, 85
Number of Illustrations: 1 b/w illustrations
Topics: Theory of Computation, Artificial Intelligence, Mathematics of Computing