We're still actively developing this site. If you encounter any issues, please report them! - Report an issue

INTRODUCTORY PROBABILITY

MATH 331
Course Description

Topics covered include axioms of probability, random variables, the most important discrete and continuous probability distributions, expectation and variance, conditional probability and conditional expectations, Markov's and Chebyshev's inequalities, laws of large numbers, and the central limit theorem. Includes a brief introduction to techniques of multivariate integration.

Prerequisties
Satisfies
Credits

3

Offered

Spring

Grade Point Average
2.77

-1.45% from Historical

Completion Rate
83.54%

-3.5% from Historical

A Rate
34.18%

14.93% from Historical

Class Size
79

203.12% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

INTRODUCTION TO THE THEORY OF PROBABILITY

MATH/STAT 431

Topics covered include axioms of probability, random variables, the most important discrete and continuous probability distributions, expectation and variance, moment generating functions, conditional probability and conditional expectations, multivariate distributions, Markov's and Chebyshev's inequalities, laws of large numbers, and the central limit theorem.

INTRODUCTION TO PROBABILITY AND MATHEMATICAL STATISTICS I

MATH/STAT 309

Probability and combinatorial methods, discrete and continuous, univariate and multivariate distributions, expected values, moments, normal distribution and derived distributions, estimation.

THEORY OF PROBABILITY I

MATH/STAT 733

An introduction to measure theoretic probability and stochastic processes.Topics include foundations, independence, zero-one laws, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, random walks, conditional expectations. Familiarity with basic measure theory (e.g.MATH 629or721) or concurrent registration inMATH 721is strongly recommended.

TOPICS IN THE THEORY OF PROBABILITY

MATH/STAT 833

Advanced topics in probability and stochastic processes.

PROBABILITY THEORY

MATH 531

A rigorous introduction to probability theory at an advanced undergraduate level. Only a minimal amount of measure theory is used, in particular, the theory of Lebesgue integrals is not needed. It is aimed at math majors and Master's degree students, or students in other fields who will need probability in their future careers. Gives an introduction to the basics (Kolmogorov axioms, conditional probability and independence, random variables, expectation) and discusses some classical results with proofs (DeMoivre-Laplace limit theorems, the study of simple random walk on the one dimensional lattice, applications of generating functions).