|Lecture (CSI 2118):||TuTh 12:30pm - 1:45pm. P. S. Krishnaprasad|
|Discussion (EGR 2112):||M 9:00am-9:50am. Biniyam Tesfaye Taddese, TA|
|PSK Office Hours (AVW 2233):||M 4:00-6:00 pm and Tu 5:00-7:00 pm.|
|TA Office Hours (EGL 1205):||We 10:30am -12:30pm|
Special Announcements(5) Typos in lecture 4 fixed. Lectures 5 and 6 posted.
(4) Applications of Bayes Theorem notes posted. There are still typos to be fixed on page 9. If you find others, please let me know.
(3) Read example on page 4 of lecture notes on conditioning, then go back to page 1 of the notes. Examine the robot merchant problem closely.
(2) Read ahead on conditional probability (pages 8-15).
(1) Read pages 1-8 of Chapter 1 of Grimmett and Stirzaker. Discussion on Monday January 29 will include this material.
Lecture Notes by P. S. Krishnaprasad
Lecture 1 (Basic Concepts)
Lecture 2 (Counting)
Lecture 3 (Conditioning)
Lecture 4 (Applications of Bayes' Theorem)
Lecture 5 (Random Variables)
Lecture 6 (Inequalities)
Homework AssignmentsGroup effort in working out homework problems is acceptable. However everyone should submit individual homework solutions. Precise credit for any sources used (colleagues, teachers, journal articles, books, web resources etc.) should be given.
Some interesting resources on the web
Wiki on Monty Hall Problem
Wiki on Bayesian Probability
Stanford Encyclopedia of Philosophy on Bayes' Theorem
Cornell site on Bayesian Inference in the Physical Sciences