CE 505-Applied Stochastic Analysis and Modeling

Spring Term, 2008

 

 2008 Catalog Data:

CE 505: Applied Stochastic Analysis and Modeling. Credit 3. Properties of continuous and discrete random variables. Analysis of stochastic processes and time series data. Linear system analysis. Field specific applications of random data analysis in engineering and physical sciences. Computational techniques in digital data analysis using Matlab. Prerequisite: Consent of instructor.

Textbook:

Ochi, K. O., Applied Probability and Stochastic Processes, Wiley, 1990. 

Reference:

Kamen, E.W. and Heck, B. S., Fundamentals of Signals and Systems using MatLab, Prentice Hall, 1997.

Coordinator:

Emre Otay, Associate Professor of Civil Engineering.

Goals:

This course is designed to give graduate students in engineering an understanding of basic concepts of stochastic analysis and prediction of random mechanical systems. 

 

Prerequisites by topic:

  1. Elementary understanding of statistics and probability
  2. Fourier transformation
  3. Knowledge of computer programming

 

Topics:

  1. Introduction to MatLab (3 classes)
  2. Properties of Fourier and Hilbert transforms (3 classes)
  3. Properties of continuous and discrete random variables (3 classes)
  4. Spectral analysis of stochastic processes (6 classes)
  5. Analysis of time series data (3 classes)
  6. Linear systems and stochastic prediction (6 classes)
  7. Extreme value analysis (3 classes)
  8. Field specific problems in stochastic analysis (6 classes)
  9. Discussion of term projects (3 classes)
  10. Tests (3 classes)

 

Computer Usage:

  1. Homework assignments and term projects require extensive use of MatLab.

 

Engineering Science: 2 credits
Engineering Design: 1 credit 

Prepared by: Emre Otay Date: February, 2008