Basic Probability and Applications
Learning Objective:By the end of this course, students should be comfortable with probability distributions and random variables. They are also expected to master many of the distribution finding techniques, such as transformation and moment generating methods, know a number of specific distributions (e.g. Bernoulli, Binomial, Poisson, Normal, t-, F-distributions etc), and understand order statistics, law of large numbers and the central limit theorem.
Description:By the end of this course, students will be able to understand probability measure, random variables, and their distribution functions, master many of the distribution finding techniques, such as transformation and moment generating methods, know a lot of special distributions such as Binomial, Poisson, normal, t-, F-distributions, and understand order statistics and the law of large numbers and the central limit theorem.
Fall 2007 Syllabus (will be similar)
Topics Covered:Set theory, classical probability, random variables, conditional probability and expectation, important inequalities, multivariate random variables, special distributions, histogram, order statistics, central limit theorem
Prerequisites:Calculus, including multivariate calculus.
Applied / Theory:10 / 90
Web Content:Syllabus, online notes
Homework:Approximately 14 homework assignments
Exams:There will be two examinations during the semester and a comprehensive final exam.
Textbooks:Official textbook information is now listed in the Schedule of Classes. NOTE: Textbook information is subject to be changed at any time at the discretion of the faculty member. If you have questions or concerns please contact the academic department.
Probability, by Jim Pitman, Springer Verlag- Available electronically to Purdue Students FREE