Articoli correlati a A User's Guide to Measure Theoretic Probability
Sinossi
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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Recensione
'A really useful book ...'. EMS Newsletter
Descrizione del libro
This 2002 book offers a rigorous probability course for a mixed audience - statisticians, biostatisticians, mathematicians, economists, and students of finance - at the advanced undergraduate/introductory graduate level, without measure theory as a prerequisite. It covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms plus more advanced topics.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
- EditoreCambridge University Press
- Data di pubblicazione2001
- ISBN 10 0521802423
- ISBN 13 9780521802420
- RilegaturaCopertina rigida
- LinguaInglese
- Numero edizione1
- Numero di pagine366
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A User's Guide to Measure Theoretic Probability (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 8)
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A User's Guide to Measure Theoretic Probability (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 8)
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A User's Guide to Measure Theoretic Probability (Hardcover)
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Hardcover. Condizione: new. Hardcover. Rigorous probabilistic arguments, built on the foundation of measure theory introduced seventy 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 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. This book offers a rigorous probability course. It covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms plus more advanced topics. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9780521802420
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A User's Guide to Measure Theoretic Probability (Hardcover)
Editore:
Cambridge University Press, Cambridge, 2001
ISBN 10: 0521802423
ISBN 13: 9780521802420
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Hardcover. Condizione: new. Hardcover. Rigorous probabilistic arguments, built on the foundation of measure theory introduced seventy 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 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. This book offers a rigorous probability course. It covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms plus more advanced topics. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780521802420
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A User's Guide to Measure Theoretic Probability
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ISBN 10: 0521802423
ISBN 13: 9780521802420
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