Logo

Lectures on Stochastic Analysis

Lectures on Stochastic Analysis
by

Publisher: University of Wisconsin
Number of pages: 119

Description:
The course will introduce stochastic integrals with respect to general semimartingales, stochastic differential equations based on these integrals, integration with respect to Poisson random measures, stochastic differential equations for general Markov processes, change of measure, and applications to finance, filtering and control. The intention has been to state the theorems correctly with all hypotheses, but no attempt has been made to include detailed proofs.

Home page url

Download or read it online for free here:
Download link
(700KB, PDF)

Similar books

Book cover: Bayesian Spectrum Analysis and Parameter EstimationBayesian Spectrum Analysis and Parameter Estimation
by - Springer
This work is a research document on the application of probability theory to the parameter estimation problem. The people who will be interested in this material are physicists, economists, and engineers who have to deal with data on a daily basis.
(19169 views)
Book cover: Applied Nonparametric RegressionApplied Nonparametric Regression
by - Cambridge University Press
Nonparametric regression analysis has become central to economic theory. Hardle, by writing the first comprehensive and accessible book on the subject, contributed enormously to making nonparametric regression equally central to econometric practice.
(27968 views)
Book cover: Design of Comparative ExperimentsDesign of Comparative Experiments
by - Cambridge University Press
This book develops a coherent framework for thinking about factors that affect experiments and their relationships, including the use of Hasse diagrams. The book is ideal for advanced undergraduate and beginning graduate courses.
(24565 views)
Book cover: Markov Chains and Mixing TimesMarkov Chains and Mixing Times
by - American Mathematical Society
An introduction to the modern approach to the theory of Markov chains. The main goal of this approach is to determine the rate of convergence of a Markov chain to the stationary distribution as a function of the size and geometry of the state space.
(15708 views)