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.
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DateTopic CoveredOther details
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Tue 8/13Finding probabilitiesPN 1.3.3
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Thu 8/15Random experimentsPN 1.3.1-1.3.2
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Tue 8/20Models of probabilityPN 1.3.4-1.3.5
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Thu 8/22Conditional probabilityPN 1.4
HW 1 due Friday -
Tue 8/27Law of total probabilityPN 1.4.2
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Thu 8/29Bayes' rulePN 1.4.3
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Tue 9/3Bayes' rule: fancier examplesPN 1.4.3
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Thu 9/5Counting strategiesPN 2.1.0-2.1.2
HW 2 due Friday -
Tue 9/10Binomial coefficientsPN 2.1.3-2.1.4
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Thu 9/12Multinomials and multisetsPN 2.1.4
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Tue 9/17Random variablesPN 3.1
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Thu 9/19Intro to Markov chainsPN 11.2.1-11.2.3, MC Section 1
HW 3 due Friday -
Tue 9/24Stationary distributionsPN 11.2.4, 11.2.6, MC Section 2
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Thu 9/26Canceled due to weather
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Tue 10/1Exam 1
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Thu 10/3Expected valuesPN 3.2.2
HW 4 due Friday -
Tue 10/8Special distributionsPN 3.1.5
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Thu 10/10TransformationsPN 3.2.3
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Tue 10/15Conditional distributionsPN 5.1.3, 5.1.5
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Thu 10/17Markov chain hitting timesPN 11.2.5, MC Section 3
HW 5 due Friday -
Tue 10/22VariancePN 3.2.4
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Thu 10/24Sums of random variablesPN 5.3.1, 6.1.2
HW 6 due Friday -
Tue 10/29Joint distributionsPN 5.1.1, 5.1.4
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Thu 10/31Exam 2
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Tue 11/5Continuous random variablesPN 4.1.0, 4.2.1, 4.2.2
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Thu 11/7Probability densitiesPN 4.1.1, 4.2.1, 4.2.2
HW 7 due Friday -
Tue 11/12Expected valuesPN 4.1.2, 4.2.3
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Thu 11/14TransformationsPN 4.1.3, 5.2.3
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Tue 11/19Mixed random variablesPN 4.3.1-4.3.2
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Thu 11/21Poisson processesPN 4.2.4, 11.1
HW 8 due Friday -
Thu 12/5Final exam (6:00pm - 8:00pm)