Instructor:  David Pollard 
Webpage:  http://www.stat.yale.edu/~pollard/Courses/241.fall2011 
When:  MWF 9:2510:15 
Where:  WLH 208 
Final Exam:  Saturday 17 December at 9:00am in room 220 Dunham Laboratory 
Office hours:  Wednesday 12:382:00 and 3:005:00. Tuesday 12:001:00, over lunch at Silliman College. Questions are also welcome immediately after each lecture. 
TA:  Sabayasachi Chatterjee (Office hour: Tuesday 1:302:30 in basement, 24 Hillhouse) 
Other help:  Residential College Math & Science Tutoring Program 
Other:  courses taught by DP in previous years 
Short description:  A first course in probability theory: probability spaces, random variables, expectations and probabilities, conditional probability, independence, some discrete and continuous distributions, central limit theorem, law of large numbers. After or concurrent with Mathematics 120a or b or equivalents. 
Grading: 

Text:  Detailed notes (in Adobe Acrobat pdf format) will be available for free from this WWW site. The 2011 notes will be similar, but not identical, to the 2005 notes. There is no prescribed text, but you might find some of the supplementary references helpful. 
Topics: 
Probability theory gives a systematic method for
describing randomness and uncertainty. This course will
explain the rules for manipulating random variables,
probabilities, and expectations, with emphasis on the
role of conditioning. The theory will be presented and
motivated through a sequence of applications, ranging
from the (traditional, boring) calculation of
probabilities for card games to (more involved, more
interesting) stochastic models.
The coin tossing model will generate the standard discrete distributions: Binomial, Poisson, geometric, negative binomial. The Poisson process, the continuous time analog of coin tossing, will generate the standard continuous distributions: exponential and gamma. Normal approximations and calculations related to the multivariate normal distribution will exercise the multivariable calculus skills of the class (or provide a crash course in multiple integrals). Applications to include topics like: Markov chains; the probability theory of games, gambling, and insurance; coding theory; queueing theory; branching processes; geometric probability and stereology; (maybe) analysis of algorithms. The coverage will be similar to, but not exactly the same as, what I have done in previous years. See the notes for 2005 for a rough guide. In fact, I will be reediting then posting those notes as the semester progresses. 