On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory - Springer Theses - Fabian Guignard - Books - Springer Nature Switzerland AG - 9783030952303 - March 13, 2022
In case cover and title do not match, the title is correct

Fabian Guignard
On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory - Springer Theses 2022 edition

Price
NZ$ 273.50
excl. VAT

Ordered from remote warehouse

Expected delivery Jul 27 - Aug 6
Get notified about new Fabian Guignard releases

Our customers say:

14-day return policy in accordance with European consumer protection law
Top ranking on Trustpilot
Add to your iMusic wish list

Not rated yet

Also available as:

Particular attention is also paid to a highly versatile exploratory data analysis tool based on information theory, the Fisher-Shannon analysis, which can be used to assess the complexity of distributional properties of temporal, spatial and spatio-temporal data sets.

158 pages, 43 Illustrations, color; 25 Illustrations, black and white; XVIII, 158 p. 68 illus., 43 i

Media Books     Hardcover Book   (Book with hard spine and cover)
Released March 13, 2022
ISBN13 9783030952303
Publishers Springer Nature Switzerland AG
Pages 158
Dimensions 242 × 163 × 17 mm   ·   420 g
Language German  

More from the same publisher

See all of Fabian Guignard ( e.g. Hardcover Book and Paperback Book )