Get Astrostatistical Challenges for the New Astronomy PDF

By Joseph M. Hilbe (auth.), Joseph M. Hilbe (eds.)

ISBN-10: 1461435072

ISBN-13: 9781461435075

ISBN-10: 1461435080

ISBN-13: 9781461435082

Astrostatistical demanding situations for the recent Astronomy offers a suite of monographs authored via numerous of the disciplines best astrostatisticians, i.e. by way of researchers from the fields of information and astronomy-astrophysics, who paintings within the statistical research of astronomical and cosmological information. 8 of the 10 monographs are improvements of shows given by means of the authors as invited or targeted subject matters in astrostatistics papers on the ISI global statistics Congress (2011, Dublin, Ireland). the outlet bankruptcy, through the editor, used to be tailored from an invited seminar given at Los Alamos nationwide Laboratory (2011) at the heritage and present kingdom of the self-discipline; the second one bankruptcy by way of Thomas Loredo used to be tailored from his invited presentation on the Statistical demanding situations in smooth Astronomy V convention (2011, Pennsylvania country University), featuring insights concerning frequentist and Bayesian equipment of estimation in astrostatistical research. the rest monographs are learn papers discussing a number of subject matters in astrostatistics. The monographs give you the reader with a great evaluation of the present kingdom astrostatistical examine, and provide directions as to matters of destiny learn. Lead authors for every bankruptcy respectively comprise Joseph M. Hilbe (Jet Propulsion Laboratory and Arizona kingdom Univ); Thomas J. Loredo (Dept of Astronomy, Cornell Univ); Stefano Andreon (INAF-Osservatorio Astronomico di Brera, Italy); Martin Kunz ( Institute for Theoretical Physics, Univ of Geneva, Switz); Benjamin Wandel ( Institut d'Astrophysique de Paris, Univ Pierre et Marie Curie, France); Roberto Trotta (Astrophysics staff, Dept of Physics, Imperial university London, UK); Phillip Gregory (Dept of Astronomy, Univ of British Columbia, Canada); Marc Henrion (Dept of arithmetic, Imperial collage, London, UK); Asis Kumar Chattopadhyay (Dept of records, Univ of Calcutta, India); Marisa March (Astrophysics workforce, Dept of Physics, Imperial collage, London, UK).

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Kuhn insisted that there could be no neutral or objective measure allowing comparison of competing paradigms in a budding scientific revolution. He based this claim on several features he found common to scientific revolutions, including the following: (1) Competing paradigms often adopt different meanings for the same term or statement, making it very difficult to effectively communicate across paradigms (a standard illustration is the term “mass,” which takes on different meanings in Newtonian and relativistic physics).

Also, the Bayesian and frequentist approaches are not rival scientific theories, but rather rival paradigms for a part of the scientific method itself (how to build and assess arguments from data to scientific hypotheses). Nevertheless, the competition between Bayesian and frequentist approaches to inference does bear one hallmark of a Kuhnian revolution: incommensurability. I believe Bayesianfrequentist incommensurability is not well-appreciated, and that it underlies multiple misconceptions about the approaches.

FMMs have long been used to handle outliers and contamination in Bayesian regression and density estimation. This work showed how to implement it with computationally expensive models and informative class membership priors. In the area of time series, the astronomer-engineer collaboration of Dobigeon, Tourneret, and Scargle [37] developed a three-level MLM to tackle joint segmentation of astronomical arrival time series (a multivariate extension of Scargle’s well-known Bayesian Blocks algorithm).

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Astrostatistical Challenges for the New Astronomy by Joseph M. Hilbe (auth.), Joseph M. Hilbe (eds.)


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