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Autor(en): 
  • David Pollard
  • A User's Guide to Measure Theoretic Probability 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  Dezember 2001  
    Genre:  Schulbücher 
     
    MATHEMATICS / Probability & Statistics / General / Probability & statistics / Probability and statistics / Probability theory; stochastic processes; Statistics / Statistics / Stochastic Processes
    ISBN:  9780521802420 
    EAN-Code: 
    9780521802420 
    Verlag:  Cambridge Academic 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 254 mm / B 178 mm / D 21 mm 
    Gewicht:  850 gr 
    Illustration:  200 exercises, Worked examples or Exercises 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Rigorous probabilistic arguments, built on the foundation of measure theory introduced eighty years ago by Kolmogorov, have invaded many fields. Students of statistics, biostatistics, econometrics, finance, and other changing disciplines now find themselves needing to absorb theory beyond what they might have learned in the typical undergraduate, calculus-based probability course. This 2002 book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. The core of the book covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms. In addition there are numerous sections treating topics traditionally thought of as more advanced, such as coupling and the KMT strong approximation, option pricing via the equivalent martingale measure, and the isoperimetric inequality for Gaussian processes. The book is not just a presentation of mathematical theory, but is also a discussion of why that theory takes its current form. It will be a secure starting point for anyone who needs to invoke rigorous probabilistic arguments and understand what they mean.

      



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