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The course is an introduction to discrete probabilities.
The main objectives are as follows:
Master basic combinatorics.
Know how to recognize common integer variables in classical modeling frameworks.
Know how to characterize, in several ways, the law of a discrete random variable. Know how to express, and if necessary calculate P(X ∈ I), E[f(X)].
Understand the notion of joint law, know how to characterize the independence of discrete variables.
Approach Markov chains on a state space ni, for simple examples: know how to decompose the state space into classes, determine the set of invariant measures, calculate a probability of reaching, and understand the long-time behavior of the law of Xn.
Schedule: 24 hours of CM + 36 hours of TD per semester (2 hrs CM + 3hrs TD per week)
Assessment Style:
Smith Students say...
What did you gain from this course?
"This course built off a lot that I had learned my first year in Discrete Math (MTH153) to give me a solid basis in studying (discrete) probabilities, including random variables, probability distributions and their expected values, variance, etc. as applied to specific scenarios."
Did the professor welcome exchange students in the course?
"No."
What do you wish you'd known before taking the course?
"I think in terms of prerequisites, if a student has taken MTH 153 (a required course for the major) they should feel ready.
Most equivalent to MTH 246 Probability."
To whom would you recommend this course?
"I'd recommend this course to a math or statistics student interested in probability and combinatorics."
What would tell a fellow MTH major/minor considering taking this course?
"I personally think having a basis in probability is important for anyone studying math, so I would recommend this course for that reason. I think it was taught in a fairly straightforward way and the topics were not too difficult to grasp."
JYA student 2023-2024
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