| 期刊全稱(chēng) | Bayesian Methods for the Physical Sciences | | 期刊簡(jiǎn)稱(chēng) | Learning from Exampl | | 影響因子2023 | Stefano Andreon,Brian Weaver | | 視頻video | http://file.papertrans.cn/182/181857/181857.mp4 | | 發(fā)行地址 | Designed for researchers and graduate students working in Astronomy and Physics who need this technical knowledge.Applicable to diverse fields, confronting measurement errors and variety in studied po | | 學(xué)科分類(lèi) | Springer Series in Astrostatistics | | 圖書(shū)封面 |  | | 影響因子 | .Statistical literacy is critical for the modern researcher in Physics and Astronomy. This book empowers researchers in these disciplines by providing the tools they will need to analyze their own data. Chapters in this book provide a statistical base from which to approach new problems, including numerical advice and a profusion of examples. The examples are engaging analyses of real-world problems taken from modern astronomical research. The examples are intended to be starting points for readers as they learn to approach their own data and research questions. Acknowledging that scientific progress now hinges on the availability of data and the possibility to improve previous analyses, data and code are distributed throughout the book. The JAGS symbolic language used throughout the book makes it easy to perform Bayesian analysis and is particularly valuable as readers may use it in a myriad of scenarios through slight modifications..This book is comprehensive, well written, and will surely be regarded as a standard text in both astrostatistics and physical statistics..Joseph M. Hilbe,. President, International Astrostatistics Association, Professor Emeritus, University of Hawaii, | | Pindex | Book 2015 |
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