Stochastic integration theory / by Péter Medvegyev
Material type: TextLanguage: English Publication details: Oxford, New York: Oxford University Press, 2007. Description: xix, 608 pISBN: 9780199215256DDC classification: 519.22 Summary: This graduate level text covers the theory of stochastic integration, an important area of Mathematics with a wide range of applications, including financial mathematics and signal processing. - ;This graduate level text covers the theory of stochastic integration, an important area of Mathematics that has a wide range of applications, including financial mathematics and signal processing. Aimed at graduate students in Mathematics, Statistics, Probability, Mathematical Finance, and Economics, the book not only covers the theory of the stochastic integral in great depth but also presents the asItem type | Current library | Call number | Status | Date due | Barcode |
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Books | PROF. BHUBANESWAR BEHERA CENTRAL LIBRARY General Stacks | 519.22 MED/S (Browse shelf(Opens below)) | Available | 137466 |
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519.2 KAR/P Point Process and Their Statistical Inference | 519.2076 SPI/S Schaum's outline of theory and problems of probability and statistics | 519.2163 BRO/I Introduction to Time Series and Forecasting | 519.22 MED/S Stochastic integration theory | 519.23 ALL/P An Introduction to Stochastic Processes with Applications to Biology | 519.23 KAN/H Handbook of Stochastic Analysis and Applications | 519.233 CHU/M Markov Chains with Stationary Transition Probabilities |
This graduate level text covers the theory of stochastic integration, an important area of Mathematics with a wide range of applications, including financial mathematics and signal processing. - ;This graduate level text covers the theory of stochastic integration, an important area of Mathematics that has a wide range of applications, including financial mathematics and signal processing. Aimed at graduate students in Mathematics, Statistics, Probability, Mathematical Finance, and Economics, the book not only covers the theory of the stochastic integral in great depth but also presents the as
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