STATS 2D03, Fall 2018
INTRODUCTION TO PROBABILITY
Many events have uncertain outcomes. Making predictions under these circumstances are risky and, in many cases, costly. A desire to improve the reliability of predictions led to the early development of probability. Today the theory of probability has become a major branch of mathematics. It also found applications in many areas including biology, communication, ecology, economics, finance, medical research, physics, and sociology. This course is an elementary introduction to the theory of probability for students in mathematics, statistics, engineering, and general sciences. By taking the course, students will get a good understanding of the basic framework and concepts of probability theory. Concrete topics include but not limited to definition of probability, counting rules, random variable, mean and variance, distribution functions, independence and dependence, and Bayesian formula.
Combinatorics, independence, conditioning; Poisson-process; discrete and continuous distributions with statistical applications; expectation, transformations moment-generating functions joint, marginal and conditional distributions; covariance and correlation; central limit theorem.
Three lectures, one tutorial; one term
Prerequisite(s): One of ARTSSCI 1D06 A/B, MATH 1AA3, 1LT3, 1NN3, 1XX3, 1ZB3, 1ZZ5 or ISCI 1A24 A/B
Not open to students with credit or registration in PSYCH 2RA3.
PLEASE REFER TO MOSAIC FOR THE MOST UP-TO-DATE INFORMATION ON TIMES AND ROOMS