CS/SE 3341 Probability and Statistics in Computer Science

MW 530 - 645 pm in room GR 4.428

 Instructor: Michael Baron Teaching Assistant: Marzana Chowdhury Teaching Assistant: Jiayi Wu Office: FO2.602-E Office: FO1.210 Office: FO1.210 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

## Homework

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

## FINAL EXAM SOLUTIONS

The Final Exam is on December 18, 5:00 - 7:00 pm. Here is a PRACTICE FINAL

The final exam covers topics:
• - 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
Notice that the 2nd part of the course is heavily based on the 1st part. So, practically, the exam is cumulative.

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.

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 corner

• 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
Brownian motion
Central Limit Theorem```

#### Recommended texts

• [KT] Probability and Statistics with Reliability, Queuing, and Computer Science Applications,
by K. Trivedi, John Wiley and Sons, New York, second edition (2002), ISBN 0471333417
• [MB] Probability and Statistics for Computer Scientists,
by M. Baron, Chapman & Hall/CRC Press (2007) or second edition (2013), ISBN 1584886412 or 1439875901
• [HK] Concepts in Probability and Stochastic Modeling,
by J. J. Higgins and S. Keller-McNulty, Wadsworth Publishing House (1995), ISBN 0-534-23136-5
• [JD] Probability and Statistics for Engineering and the Sciences, seventh edition (2008) or eighth edition (2011),
by J. L. Devore, Duxbury, ISBN 0495557447 or 0538733527
These texts overlap, so you don't need to buy all of them. When choosing the textbook, notice that...
• [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.
All four textbooks are written as the first course in Probability and Statistics and assume your knowledge and working skills of Calculus I.