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Autor(en): 
  • Jaffe Andrew H.
  • The Random Universe: How Models and Probability Help Us Make Sense of the Cosmos 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  November 2025  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Albert Einstein / Arthur Edington / Astrophysics / Bayesian Inference / Bayesian interference / Chaos / Cosmology / Cosmology & the universe
    ISBN:  9780300250503 
    EAN-Code: 
    9780300250503 
    Verlag:  University Presses 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 156 mm / D  
    Seiten:  288 
    Illustration:  28 b-w illus. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    An award-winning astrophysicist looks at how the understanding of uncertainty and randomness has led to breakthroughs in our knowledge of the cosmos
     
    All of us understand the world around us by constructing models, comparing them to observations, and drawing conclusions. Scientists create, test, and replace these models by applying the twinned concepts of probability and randomness. Exploring how this process has refined our knowledge of quantum mechanics and the birth of the universe, Andrew H. Jaffe offers a unique synthesis of the philosophy of epistemology, the mathematics of probability, and the science of cosmology.
     
    As Jaffe puts Enlightenment thinkers like David Hume in conversation with contemporary philosophers such as Karl Popper and Imre Lakatos and engages with scientists ranging from Isaac Newton and Galileo to Albert Einstein and Arthur Eddington, he uses Thomas Bayes’s seminal studies of statistics and probability to make sense of conflicting currents of thought. This is a deep look into how we have learned to account for uncertainty in our search for knowledge—and a reminder that science is not about facts and data as such but about creating models that correctly account for those facts and data.

      



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