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Sophia L. Kalpazidou - Cycle Representations of Markov Processes - 9781441921215 - V9781441921215
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Cycle Representations of Markov Processes

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Description for Cycle Representations of Markov Processes paperback. Series: Stochastic Modelling and Applied Probability. Num Pages: 324 pages, 17 black & white illustrations, biography. BIC Classification: PBWL. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 17. Weight in Grams: 482.
The cycle representations of Markov processes have been advanced after the publication of the ?rst edition to many directions. One main purpose of these advances was the revelation of wide-ranging interpretations of the - cle decompositions of Markov processes such as homologic decompositions, orthogonality equations, Fourier series, semigroup equations, disinteg- tions of measures, and so on, which altogether express a genuine law of real phenomena. The versatility of these interpretations is consequently motivated by the existence of algebraic-topological principles in the fundamentals of the - clerepresentationsofMarkovprocesses,whicheliberatesthestandardview on the Markovian modelling to new intuitive and constructive approaches. For instance, the ruling ... Read more

Product Details

Format
Paperback
Publication date
2010
Publisher
Springer United States
Number of pages
324
Condition
New
Series
Stochastic Modelling and Applied Probability
Number of Pages
304
Place of Publication
New York, NY, United States
ISBN
9781441921215
SKU
V9781441921215
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15

Reviews for Cycle Representations of Markov Processes
From the reviews of the second edition: "The first edition of this book [S. Kalpazidou, Cycle representations of Markov processes, Springer, New York, 1995; MR1336140 (96g:60002)] has been reviewed by this reviewer. This second edition amplifies Part I of the first edition, which consisted of 7 chapters, by adding four chapters: Chapter 8. Cycloid Markov processes; Chapter 9. ... Read more

Goodreads reviews for Cycle Representations of Markov Processes


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