SpringerBriefs in Computer Science

Ryabko, Daniil

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

Published by: Springer

Publication Date: 2020-09-27

Format: Paperback

ISBN-13: 9783030543037

DOI: 10.1007/978-3-030-54304-4

Dimensions: 235cm x155cm

Pages: 85