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Universal Time-Series Forecasting with Mixture Predictors de Daniil Ryabko

Universal Time-Series Forecasting with Mixture Predictors: SpringerBriefs in Computer Science

Autor Daniil Ryabko
en Limba Engleză Paperback – 27 sep 2020
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.
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Specificații

ISBN-13: 9783030543037
ISBN-10: 303054303X
Pagini: 85
Ilustrații: VIII, 85 p. 1 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.15 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seria SpringerBriefs in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Introduction.- Notation and Definitions.- Prediction in Total Variation: Characterizations.- Prediction in KL-Divergence.- Decision-Theoretic Interpretations.- Middle-Case: Combining Predictors Whose Loss Vanishes.- Conditions Under Which One Measure Is a Predictor for Another.- Conclusion and Outlook.

Recenzii

“It is a very useful book for graduate students and researchers who are interested in the problem of sequential prediction.” (Lei Jin, Mathematical Reviews, November, 2022)

“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)

Notă biografică

Dr. Daniil Ryabko (HDR) has a full-time position at INRIA, he has recently been on research assignments in Belize and Madagascar.

Textul de pe ultima copertă

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.

Caracteristici

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