Probability and Statistics in Computer Science
MW 530 - 645 pm
in room GR 4.428
| Phone: ||972-UTD-6874|| ||
Office hours:||Monday 12:00-2:00 pm ||
Office hours:||Tuesday 3:30-5:30 pm
|Office hours: ||MW 4:15-5:15 pm|
Course Syllabus - schedule, office hours, tips, policies
eLearning - check your grades and join discussion groups
Practice Homework 1
Practice Homework 2
Practice Homework 3
Practice Homework 4
Practice Homework 5
Practice Homework 6
Practice Homework 7
Practice Homework 8
Practice Homework 9
Practice Homework 10
Practice Homework 11
Practice Homework 12
These sets of training problems are also available on WeBWork. The graded problems
appear on WeBWork only.
Quizzes and Exams
Quiz 1, solutions
Quiz 2, solutions
Quiz 3, solutions
Quiz 4, solutions
Midterm Exam, solutions
Quiz 5, solutions
Quiz 6, solutions
Quiz 7, solutions
Quiz 8, solutions
Quiz 9, solutions
Quiz 10, solutions
The Final Exam is on December 18, 5:00 - 7:00 pm.
Here is a PRACTICE FINAL
Ready for more practice? Here are additional exercises.
The final exam covers topics:
Notice that the 2nd part of the course is heavily based on the 1st part.
So, practically, the exam is cumulative.
- - Stochastic processes; counting processes - Binomial, Poisson processes
- - Markov chains - transition probabilities, steady-state distribution
- - Queuing processes - Bernoulli, M/M/1
- - Statistics: parameter estimation, confidence intervals, hypothesis testing
Here is the Cheat Sheet for the Final Exam
which will be attached to your exams along with the tables of distributions. No other material is allowed on the exam.
GRADES ARE HERE.
Learn how to manage exam stress.
Some Lecture Notes
(You may need Acrobat Reader to see and print these notes)
Notes 1 "Introduction. Probability rules."
Notes 2 "Equally likely outcomes. Conditional probability"
Notes 3 "Random variables and distributions"
Notes 4 "Discrete distributions"
Tables of Distributions
Notes 5 "Continuous distributions"
Notes 6 "Important continuous distributions" (updated on Feb 28)
Notes 7 "Stochastic processes. Bernoulli, Binomial, Poisson processes."
Notes 8 "Markov chains"
Notes 9 "Single-server queuing systems"
Notes 10 "Statistical inference"
MATLAB programs used in our classroom demonstrations:
Markov chain for sunny and cloudy days
Markov chain for the game of ladder
Poisson process of arrivals
Bernoulli and Binomial processes
Central Limit Theorem
These texts overlap, so you don't need to buy all of them.
When choosing the textbook, notice that...
Probability and Statistics with Reliability, Queuing, and Computer Science Applications,
by K. Trivedi, John Wiley and Sons, New York, second edition (2002), ISBN 0471333417
Probability and Statistics for Computer Scientists,
by M. Baron, Chapman & Hall/CRC Press (2007) or second edition (2013), ISBN 1584886412 or 1439875901
Concepts in Probability and Stochastic Modeling,
by J. J. Higgins and S. Keller-McNulty, Wadsworth Publishing House (1995), ISBN 0-534-23136-5
Probability and Statistics for Engineering and the Sciences, seventh edition (2008) or
eighth edition (2011),
by J. L. Devore, Duxbury, ISBN 0495557447 or 0538733527
All four textbooks are written as the first course in Probability and Statistics and assume your
knowledge and working skills of Calculus I.
- [KT] covers all the topics of our course and additional material on Markov chains, queuing theory,
and regression. It is written at a slightly higher mathematical level and does not contain too many
exercises. It has been recently used for this course.
- [MB] covers all the topics of our course at the junior/senior level and has additional material on
computer simulations and Statistics. It has many examples and exercises in each chapter.
- [HK] covers all the topics except Statistics at the junior/senior level. Has some good examples and
exercises in each chapter. It has been used for this course in the past.
- [JD] covers all the topics except Stochastic processes, Markov chains, and queuing theory at the
junior/senior level. It has many examples and exercises in each chapter and contains additional
material on statistical inference, regression, and analysis of variance.