Beginning with the concept of random processes and Brownian motion and building on the theory and research directions in a self-contained manner, this book provides an introduction to stochastic analysis for graduate students, researchers and applied scientists interested in stochastic processes and their applications.
Beginning with the concept of random processes and Brownian motion and building on the theory and research directions in a self-contained manner, this book provides an introduction to stochastic analysis for graduate students, researchers and applied scientists interested in stochastic processes and their applications.
Gopinath Kallianpur, Professor Emeritus at University of North Carolina at Chapel Hill, has worked extensively on Stochastic Analysis and is a world renowned expert on stochastic filtering theory. He is the author of Stochastic Filtering Theory, and a co-author of White Noise Theory of Prediction, Filtering and Smoothing, Introduction to Option Pricing Theory , and Stochastic Differential Equations in Infinite Dimensions. P. Sundar is a Professor of Mathematics at Louisiana State University. He works on Stochastic Analysis, and is on the Editorial Board for the journal Communications on Stochastic Analysis. He has co-edited a book titled Infinite Dimensional Stochastic Analysis.
Inhaltsangabe
1: Introduction to Stochastic Processes 2: Brownian Motion and Wiener Measure 3: Elements of Martingale Theory 4: Analytic Tools for Brownian Motion 5: Stochastic Integration 6: Stochastic Differential Equations 7: The Martingale Problem 8: Probability Theory and Partial Differential Equations 9: Gaussian Solutions 10: Jump Markov Processes 11: Invariant Measures and Ergodicity 12: Large Deviations for Diffusions
1: Introduction to Stochastic Processes 2: Brownian Motion and Wiener Measure 3: Elements of Martingale Theory 4: Analytic Tools for Brownian Motion 5: Stochastic Integration 6: Stochastic Differential Equations 7: The Martingale Problem 8: Probability Theory and Partial Differential Equations 9: Gaussian Solutions 10: Jump Markov Processes 11: Invariant Measures and Ergodicity 12: Large Deviations for Diffusions
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