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This book gives a straightforward introduction to the field as it is nowadays required in many branches of analysis and especially in probability theory. The first three chapters (Measure Theory, Integration Theory, Product Measures) basically follow the clear and approved exposition given in the author's earlier book on ""Probability Theory and Measure Theory"". Special emphasis is laid on a complete discussion of the transformation of measures and integration with respect to the product show more measure, convergence theorems, parameter depending integrals, as well as the Radon-Nikodym theorem. show lessTags
Member Reviews
A truly remarkable introductory text on abstract measure theory. The topics and results are well-organized. The author writes with precision and rarely skips steps. I've gone through the book and carefully filled in the gaps in detail, and that took 20 pages. This may sound like quite a lot, but it really isn't. The last chapter does draw upon a substantial number of results from introductory general topology. When I typed up complete notes for this background material (including detailed proofs of Zorn's Lemma and the Tychonoff Theorem), it took me another 24 pages. It should be said that a lot of the stuff in that final chapter goes well beyond what is typically covered in a one-semester measure theory course. For example, it includes show more the Portmanteau Theorem and Prohorov's Theorem, which are usually considered to be results of probability theory. show less
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Author Information
Series
Common Knowledge
- Canonical title
- Measure and Integration Theory
Classifications
Statistics
- Members
- 16
- Popularity
- 1,387,898
- Reviews
- 1
- Rating
- (3.75)
- Languages
- English, German
- Media
- Paper, Ebook
- ISBNs
- 7







