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Bayesian Models for Astrophysical Data: Using R, JAGS, Python, and Stan by [Hilbe, Joseph M., de Souza, Rafael S., Ishida, Emille E. O.]
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Bayesian Models for Astrophysical Data: Using R, JAGS, Python, and Stan 1st Edition, Kindle Edition

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Product description


'This volume is a very welcome addition to the small but growing library of resources for advanced analysis of astronomical data. Astronomers are often confronted with complex constrained regression problems, situations that benefit from computationally intensive Bayesian approaches. The authors provide a unique and sophisticated guide with tutorials in methodology and software implementation. The worked examples are impressive. Many astronomers use Python and will benefit from the less familiar capabilities of R, Stan, and JAGS for Bayesian analysis. I suspect the work will also be useful to scientists in other fields who venture into the world of Bayesian computational statistics.' Eric D. Feigelson, Pennsylvania State University, author of Modern Statistical Methods for Astronomy

'Encyclopaedic in scope, a treasure trove of ready code for the hands-on practitioner.' Ben Wandelt, Paris Institute of Astrophysics, Institut Lagrange de Paris, Université Paris-Sorbonne

'This informative book is a valuable resource for astronomers, astrophysicists, and cosmologists at all levels of their career. From students starting out in the field to researchers at the frontiers of data analysis, everyone will find insightful techniques accompanied by helpful examples of code. With this book, Hilbe, de Souza, and Ishida are firmly taking astrostatistics into the twenty-first century.' Roberto Trotta, Imperial College London, author of The Edge of the Sky

Book Description

This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. A must-have for astronomers, its concrete focus on modeling, analysis, and interpretation will also be attractive to researchers in the sciences more broadly.

Product details

  • Format: Kindle Edition
  • File Size: 10094 KB
  • Print Length: 408 pages
  • Simultaneous Device Usage: Up to 4 simultaneous devices, per publisher limits
  • Publisher: Cambridge University Press; 1 edition (30 April 2017)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Enhanced Typesetting: Enabled
  • Average Customer Review: Be the first to review this item
  • Amazon Bestsellers Rank: #1,972,799 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Most helpful customer reviews on 5.0 out of 5 stars 2 reviews
Cannon Gray LLC
5.0 out of 5 starsYou don't need to be an astrophysicist to benefit from the book
27 June 2017 - Published on
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5 people found this helpful.
M. Dantas
5.0 out of 5 starsOne of a kind
2 January 2018 - Published on
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