Statistics, Data Mining, and Machine Learning in Astronomy (notice n° 72673)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02672cam a2200301zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88877995 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250107232812.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250108s2019 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780691198309 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88877995 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Ivezic, Zeljko |
| 245 01 - TITLE STATEMENT | |
| Title | Statistics, Data Mining, and Machine Learning in Astronomy |
| Remainder of title | A Practical Python Guide for the Analysis of Survey Data, Updated Edition |
| Statement of responsibility, etc. | ['Ivezic, Zeljko', 'Connolly, Andrew J.', 'Vanderplas, Jacob T.'] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | Princeton University Press |
| Date of production, publication, distribution, manufacture, or copyright notice | 2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest. An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date. Fully revised and expanded Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from astronomical surveys Uses a freely available Python codebase throughout Ideal for graduate students, advanced undergraduates, and working astronomers |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ivezic, Zeljko |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Connolly, Andrew J. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Vanderplas, Jacob T. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88877995">https://international.scholarvox.com/netsen/book/88877995</a> |
| Electronic format type | text/html |
| Host name | |
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