2010 Spring MATH 135-01
Bulletin Course Description Probability models, random variables with discrete and continuous distributions. Independence, joint distributions, conditional distributions. Expectations, functions of random variables, central limit theorem. Instructor: Staff
(Instructor named in bulletin description above may not be current. For current instructor, see listing below.)
Title PROBABILITY Department MATH Course Number 2010 Spring 135 Section Number 01 Primary Instructor Charbonneau,Benoit Prerequisites Prerequisite: Mathematics 102, 103, or 105.
Synopsis of course content
The course starts with the marbles in jars level of probability, but by the end we'll have explored a wide variety of topics in modern probability. Many people have seen continuous distributions in the form of normal (aka Gaussian, aka bell-curve) distributions. In addition to normals, we'll study uniforms, exponentials, gammas, betas, and Cauchy distributions. Discrete distributions tackled include Bernoulli, binomial, Poisson, geometric and negative binomial. A large part of the course will be the concept of conditioning: how probabilities change when additional information is introduced into a system. Also tackled are topics such as expectation and standard deviation and important theorems such as the Strong Law of Large Numbers and the Central Limit Theorem.
Textbooks
Pitman, Probability, ISBN 0387979743
Assignments
A homework assignment will be due every week.
Exams
There will be two midterms and a final exam. The final will be cumulative. There will be occasional quizzes.
Term Papers
There will be no term papers.
Grade to be based on
Homework: 10%
Quiz: 15%
Midterms 20% each
Final: 35%
