Contact

Phone: 470-578-7235

Email: mlavrov@kennesaw.edu

Office Location: D 245

Office Hours:
Check the course pages for information about office hours for my classes, or send me an email to make an appointment.

Math 3332: Probability Theory (Fall 2024)

General Information

Instructor: Mikhail Lavrov
Location: Mathematics 116
Lecture times: 6:30pm to 7:45pm on Tuesday and Thursday
Textbook: Introduction to Probability, Statistics, and Random Processes by Hossein Pishro-Nik, available online at https://www.probabilitycourse.com/. 
Office hours: 12:00pm to 2:00pm on Wednesday..
D2L page: https://kennesaw.view.usg.edu/d2l/home/3287993.

D2L will be used to submit assignments (these will be posted both here and on D2L, for convenience) and to view grades. The syllabus will also be posted there.

During the scheduled office hours, you should feel free to show up with no notice if you have questions of any kind.

If it turns out you are not available during that time, begin by emailing me; if your questions are easy to answer by email, I will do that, and if not, we can find another time to meet. (Allow some time for me to check my email.)

Homework and Exams

There will be eight homework assignments, two midterm exams, and one final exam; the dates are marked below.

I will post the homework assignments here and on D2L; they are always due on Friday at 11:59pm, via D2L.

Midterm exams will be given in person during our ordinary 75-minute class period.

Detailed Schedule

A note in the format "PN 1.2.3" is a reference to section 1.2.3 of the official textbook (linked above). I strongly encourage reading the textbook to supplement lectures, and working through the many problems included in the textbook.

Section 11.2 of the textbook has some material on Markov chains, but I've also written my own notes on Markov chains to supplement it: markov-chains.pdf. The letters MC indicate references to this document.

  • Date
    Topic Covered
    Other details
  • Tue 8/13
    Finding probabilities
    PN 1.3.3
  • Thu 8/15
    Random experiments
    PN 1.3.1-1.3.2
  • Tue 8/20
    Models of probability
    PN 1.3.4-1.3.5
  • Thu 8/22
    Conditional probability
    PN 1.4
    HW 1 due Friday
  • Tue 8/27
    Law of total probability
    PN 1.4.2
  • Thu 8/29
    Bayes' rule
    PN 1.4.3
  • Tue 9/3
    Bayes' rule: fancier examples
    PN 1.4.3
  • Thu 9/5
    Counting strategies
    PN 2.1.0-2.1.2
    HW 2 due Friday
  • Tue 9/10
    Binomial coefficients
    PN 2.1.3-2.1.4
  • Thu 9/12
    Multinomials and multisets
    PN 2.1.4
  • Tue 9/17
    Random variables
    PN 3.1
  • Thu 9/19
    Intro to Markov chains
    PN 11.2.1-11.2.3, MC Section 1
    HW 3 due Friday
  • Tue 9/24
    Stationary distributions
    PN 11.2.4, 11.2.6, MC Section 2
  • Thu 9/26
    Exam 1
     
  • Tue 10/1
    Expected values
    PN 3.2.2
  • Thu 10/3
    Special distributions
    PN 3.1.5
    HW 4 due Friday
  • Tue 10/8
    Transformations
    PN 3.2.3
  • Thu 10/10
    Conditional distributions
    PN 5.1.3, 5.1.5
  • Tue 10/15
    Markov chain hitting times
    PN 11.2.5, MC Section 3
  • Thu 10/17
    Variance
    PN 3.2.4
    HW 5 due Friday
  • Tue 10/22
    Sums of random variables
    PN 5.3.1, 6.1.2
  • Thu 10/24
    Joint distributions
    PN 5.1.1, 5.1.4
    HW 6 due Friday
  • Tue 10/29
    (Flex time for previous topics)
     
  • Thu 10/31
    Exam 2
     
  • Tue 11/5
    Continuous random variables
    PN 4.1.0, 4.2.1, 4.2.2
  • Thu 11/7
    Probability densities
    PN 4.1.1, 4.2.1, 4.2.2
    HW 7 due Friday
  • Tue 11/12
    Expected values
    PN 4.1.2, 4.2.3
  • Thu 11/14
    Transformations
    PN 4.1.3, 5.2.3
  • Tue 11/19
    Mixed random variables
    PN 4.3.1-4.3.2
  • Thu 11/21
    Poisson processes
    PN 4.2.4, 11.1
    HW 8 due Friday
  • Thu 12/5
    Final exam (6:00pm - 8:00pm)
     

 

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