Syllabus for Math 351, Section E1 (Spring 2002)

Course topics: Stochastic simulation.  This is an attempt to start a course in stochastic simulation.  Since neither of the instructors are experts in this, the attempt
may be at times rocky.  Nevertheless, we hope it will be beneficial.  There are a large number of places in science and engineering where probabilistic simulation
is a powerful tool in evaluating expressions.  Random walks are used in economics and finance.  Molecular dynamics are used in materials science to simulate
the behavior of large systems of particles via high-dimensional stochastic differential equations.  While we won't be able to cover much of these high-end problems,
we will attempt to introduce a number of concepts which may be of use in a scientific or engineering career.  While it would be beneficial for the students to have
Math 361, we will be content with a certain amount of `mathematical maturity'.

Grading policy: The grades will be entirely based on computer projects.  These will be assigned as the semester progresses.   We will assume that you can cope with Mathematica
and C and Java.  We do not require expertise, only the ability to get along.  As we mentioned above, it is entirely likely that the students will be more competent in programming than the instructors.
Note:  the projects will only be gradeable if they can run on the Math network (which has Mathematica, Java, and various forms of C).  We will sort all of this out as we go along.
Since the class is relatively small, our pedagogical goal will be to together explore the subject.

Books:  We have

  • The Mathematica Book
  • Mathematica 3.0 Standard Add-on Packages
  • Java for Engineers and Scientists by Stephen Chapman (Prentice-Hall, 2000)
  • The C++ Programming Language by Bjarne Stroustrup (Addison Wesley, 1997)
  • Mathematica has great online documentation, so perhaps you don't need to buy that book.  The C++ book is perhaps too compendious for our needs---there are undoubtedly pithier books.
    The Java book is sort of good, but it has gaps.  A good place for elementary probability material is

    Tentative Course Outline