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 (Spring 2025)

General Information

Instructor: Mikhail Lavrov
Location: Mathematics 116
Lecture times: 3:30pm to 4:45pm on Monday and Wednesday
Textbook: Introduction to Probability, Statistics, and Random Processes by Hossein Pishro-Nik, available online at https://www.probabilitycourse.com/. 
Office hours: 2:15pm to 3:15pm on Monday and Wednesday, in my office (Mathematics 245)
D2L page: https://kennesaw.view.usg.edu/d2l/home/3428777.

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); for convenience, the link goes directly to the referenced section. 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
  • Mon 1/6
    Finding probabilities
  • Wed 1/8
    Random experiments
  • Mon 1/13
    Models of probability
  • Wed 1/15
    Conditional probability
    PN 1.4
    HW 1 due Friday
  • Mon 1/20
    No class
     
  • Wed 1/22
    Law of total probability
  • Mon 1/27
    Bayes' rule
  • Wed 1/29
    Bayes' rule: fancier examples
    PN 1.4.3
    HW 2 due Friday
  • Mon 2/3
    Counting strategies
  • Wed 2/5
    Binomial coefficients
  • Mon 2/10
    Multinomials and multisets
  • Wed 2/12
    Flex time
    HW 3 due Friday
  • Mon 2/17
    Exam 1
     
  • Wed 2/19
    Random variables
    PN 3.1
  • Mon 2/24
    Intro to Markov chains
    PN 11.2.1-11.2.3, MC section 1
  • Wed 2/26
    Stationary distributions
    PN 11.2.4, 11.2.6, MC section 2
    HW 4 due Friday
  • Mon 3/3
    Expected values
  • Wed 3/5
    Special distributions
  • Mon 3/10
    No class
     
  • Wed 3/12
    No class
     
  • Mon 3/17
    Transformations
  • Wed 3/19
    Conditional distributions
    PN 5.1.3, 5.1.5
    HW 5 due Friday
  • Mon 3/24
    Variance
  • Wed 3/26
    Markov chain hitting times
    PN 11.2.5, MC section 3
  • Mon 3/31
    Sums of random variables
  • Wed 4/2
    Joint distributions
    PN 5.1.1, 5.1.4
    HW 6 due Friday
  • Mon 4/7
    Exam 2
     
  • Wed 4/9
    Continuous random variables
  • Mon 4/14
    Probability densities
  • Wed 4/16
    Expected values
    PN 4.1.2, 4.2.3
    HW 7 due Friday
  • Mon 4/21
    Transformations
  • Wed 4/23
    Conditional+joint distributions
  • Mon 4/28
    Poisson processes
    PN 4.2.4, 11.1.1-11.1.3
    HW 8 due Monday
  • Wed 4/30
    No class
     
  • Mon 5/5
    Final exam (3:30pm - 5:30pm)
     

 

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