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"As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. The updates in this new edition will include fixing "code rot," correcting errata, and adding some new sections. In particular, the new sections include new material on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) 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, evaluate the methods, and adapt them to their own fields of interest"
Astronomy --- Statistical astronomy. --- Python (Computer program language) --- Astronomie --- Statistique stellaire. --- Python (langage de programmation) --- Data processing. --- Informatique.
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This how-to guide presents today's most complete coverage of performing, interpreting, and reporting post-mortem examinations. In addition to discussing the basics of the specialty, this lasting and useful reference features information on the performance of specialized autopsy procedures. The material is divided into two sections for ease of use: a manual covering specific autopsy procedures, biosafety, generation of autopsy reports, preparation of death certificates, and other essential subjects; and an atlas, organized by organ system, that captures the appearance of the complete spectrum of autopsy findings.
Autopsy --- Death Certificates --- Quality Control --- methods --- Death Certificates. --- Quality Control. --- methods. --- Autopsia --- Control de calidad --- Libros electrónicos --- Necropsy --- Necroscopy --- Post-mortem examinations --- Postmortem examinations --- Postmortems --- Anatomy, Pathological --- Dead bodies (Law) --- Human dissection --- Medical jurisprudence --- Death --- Causes
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"Statistics, Data Mining, and Machine Learning in Astronomy" presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) 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, evaluate the methods, and adapt them to their own fields of interest.
Astronomy --- Statistical astronomy --- Astronomie --- Statistique stellaire. --- Data processing --- Informatique.
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A mainstay for pathology residents, Autopsy Pathology is designed with a uniquely combined manual and atlas format that presents today's most complete coverage of performing, interpreting, and reporting post-mortem examinations. This lasting and useful medical reference book offers a practical, step-by-step approach to discussing not only the basics of the specialty, but the performance of specialized autopsy procedures as well.
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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 expandedDescribes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data setsFeatures real-world data sets from astronomical surveysUses a freely available Python codebase throughoutIdeal for graduate students, advanced undergraduates, and working astronomers
Astronomy --- Statistical astronomy. --- Python (Computer program language) --- Data processing.
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