Cantitate/Preț
Produs

Practical Time Series Data Analysis: Cambridge Observing Handbooks for Research Astronomers

Autor Jeffrey Scargle
en Limba Engleză Hardback – 31 dec 2026
This practical guide demonstrates the use of methods to analyze sequential data, from basic standard methods to advanced novel techniques, all in the setting of an accessible, intuitive view of underlying statistical theory. The book reveals the unappreciated limitations of standard methods and shows how simple new viewpoints can overcome these obstacles and open up novel opportunities for discovery. Readers, from beginning students of astronomy, physics, statistics, and other technical subjects, to seasoned practitioners in these fields, are invited to use thought-provoking exercises to delve deeper into important topics without resorting to mindless calculations. Several case studies are included - not only to point out the end results, but to illustrate how the scientific process is actually carried out in practice. Scargle, a well-known pioneer in the field, shares his decades of experience to demonstrate improvements and extensions of classical techniques and discourage uncritical use of 'black box' analyses.
Citește tot Restrânge

Preț: 43492 lei

Precomandă

Puncte Express: 652

Carte nepublicată încă

Livrare prin curier în România Precomanda se expediază când titlul devine disponibil.
Transport gratuit pentru acest produs Plată online sau ramburs, în funcție de opțiunile comenzii.
Retur gratuit în 14 zile Comandă securizată și suport în română.
Doresc să fiu notificat când acest titlu va fi disponibil:

Specificații


Notă biografică

Jeffrey Scargle has developed practical data analysis methods and applied them to astronomical systems ranging from the Sun, the rings of Saturn, exoplanets, active galaxies, gamma-ray bursts, and the cosmic web. The Lomb-Scargle periodogram and the Bayesian Block algorithm, applicable to all data modes and with various generalizations, are in wide use in astronomical time series analysis.

Cuprins

Part I. Time Series Data: 1. The dynamic universe; 2. Statistical and related issues; 3. The many modes of time series; 4. Uncertainty in time series data; 5. Previewing the data; 6. Preprocessing the data; 7. Classical summary statistics and beyond; 8. Preprocessing point measurements; Part II. Time Series Analysis: 9. Optimal segmentation; 10. Bayesian blocks; 11. Linear transforms; 12. Periodograms and omnigrams; 13. Correlation functions; 14. Random processes; 15. Classical time series models; 16. Beyond classical models; 17. Theory and observation meet; 18. Case studies; Glossary, References; Index.