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Unit information: Intoduction To Stochastic Analysis in 2014/15

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Unit name Intoduction To Stochastic Analysis
Unit code MATHM0017
Credit points 20
Level of study M/7
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Professor. Balint Toth
Open unit status Not open
Pre-requisites

Any two of the following three: Probability 3 (MATH 35700) [includes: Applied Probability 2 (MATH 21400)] Measure Theory and Integration (MATH 34000) [includes: Metric Spaces (MATH 20200)] Functional Analysis 3 (MATH 36202) [includes: Metric Spaces (MATH 20200)]

Co-requisites

None.

School/department School of Mathematics
Faculty Faculty of Science

Description including Unit Aims

Unit aims

The aim of the unit is to introduce theory of Brownian motion, continuous martingales, stochastic integration, stochastic differential equations and diffusion processes. With particular emphasis on applications to physical sciences, financial mathematics and other branches of applied mathematics.

General Description of the Unit

The course is intended for (post)graduate students of pure and applied mathematics with a sufficiently strong background in analysis. Construction and analytic properties of Brownian motion, stochastic integration a la Ito, stochastic differential equations and their strong and weak solutions, various approaches to diffusion processes will be covered. These are all topics of central importance in the general advanced mathematical culture. Special emphasis will be put on various applications of the theory. The course is recommended to all mathematics (post)graduate students with a broad view.

Further information is available on the School of Mathematics website: http://www.maths.bris.ac.uk/study/undergrad/

Intended Learning Outcomes

Learning Objectives

To gain profound understanding of the basic notions and techniques of the theory of:

Brownian motion; stochastic differential equations and their strong and weak solutions; diffusion processes; Applications of these concepts.

To prepare the postgraduate student for independent research in mathematics.

Teaching Information

Lectures supported by problem sheets and solution sheets.

Assessment Information

80% Examination and 20% Coursework.

Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.

Reading and References

  • K.L. Chung, R. Williams: Introduction to stochastic integration. Second edition. Birkauser, 1989
  • I. Karatzas, S. Shreve: Brownian Motion and Stochastic Calculus, Springer 1991
  • F. Klebaner: Introduction to Stochastic Calculus With Applications, World Scientific, 2005
  • J. Lamperti, Stochastic Processes: a Survey of the Mathematical Theory, Springer 1977
  • B. Oksendal, Stochastic Differential Equations: An Introduction with Applications, 6th edition, Springer 2010
  • Instructor’s lecture notes and problem sheets

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