Prerequisites: Introductory probability (Math 244 or 333),
numerical methods (Math 340),
and the ability to program a computer in a language such as
Fortran or C.
Examinations: There will be a midterm examination
and a final examination.
The midterm examination will occur before the "drop'' deadline. The final
examination date, time, and location will be determined by the university.
Homework: Homework assignments/projects will be given frequently;
many will involve writing computer programs in a computer language
such as C or Fortran.
Each assignment must be turned in at the beginning of class. Late
assignments are NOT accepted. Early assignments are always welcomed
and are appropriate for preplanned absences from class.
Even though not every problem in an assignment may be graded,
you are expected to attempt all of them.
As a standing assignment,
you should read the relevant sections of the textbook prior to lecture.
Quizzes: From time to time, quizzes may be given. Make up
quizzes are NOT given.
Grading: The midterm examination will represent 35% of your grade.
The final examination will also be worth 35% of your grade. The remaining
30% of your grade will be determined by your homework/quizzes/projects;
in calculating this quantity, I will drop your one lowest homework or
quiz score from throughout the semester.
Attendance: Attendance at and participation in all lectures
is expected. If you know in advance that you will be absent from
class for a legitimate reason, please tell me prior to your absence
so that appropriate arrangements (if any) can be made.
Tardiness to class is very disruptive of the classroom environment
and should be avoided.
Academic Integrity Code: The
NJIT Academic Integrity Code applies
to all activities associated with the course, including but not limited to
homework, quizzes, examinations, and projects. As an example, when you submit
a homework assignment, you are certifying that your paper contains only
your work and is not copied from other people or sources.
Course Topics:
Major topics for this course include:
Review of basic probability, generation of pseudorandom
numbers, Monte Carlo integration
Simulation of random samples from discrete distributions
and continuous distributions
Discrete event simulation for stochastic models
of queueing systems and financial problems
Analysis, verification, and validation
of simulation results