# Difference between revisions of "Undergraduate courses in probability"

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Probability theory is ubiquitous in natural science, social science and engineering, so this course can be valuable in conjunction with many different majors. 431 is not a course in statistics. Statistics is a discipline mainly concerned with analyzing and representing data. Probability theory forms the mathematical foundation of statistics, but the two disciplines are separate. | Probability theory is ubiquitous in natural science, social science and engineering, so this course can be valuable in conjunction with many different majors. 431 is not a course in statistics. Statistics is a discipline mainly concerned with analyzing and representing data. Probability theory forms the mathematical foundation of statistics, but the two disciplines are separate. | ||

− | 531 - Probability theory | + | |

+ | '''531 - Probability theory''' | ||

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+ | The course is a rigorous introduction to probability theory on an advanced undergraduate level. Only a minimal amount of measure theory is used (in particular, Lebesgue integrals will not be needed). The course gives an introduction to the basics (Kolmogorov axioms, conditional probability and independence, random variables, expectation) and discusses some of the classical results of probability theory with proofs (DeMoivre-Laplace limit theorems, the study of simple random walk on Z, applications of generating functions). | ||

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+ | This course is currently in development. The pilot version of the course will run in the Spring 2015 semester as [https://www.math.wisc.edu/491a-topics-probability-theory 491a - Topics : Probability Theory] | ||

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[https://www.math.wisc.edu/632-introduction-stochastic-processes 632 - Introduction to stochastic processes] | [https://www.math.wisc.edu/632-introduction-stochastic-processes 632 - Introduction to stochastic processes] |

## Revision as of 12:02, 21 August 2014

**431 - Introduction to the theory of probability**

Math 431 is an introduction to probability theory, the part of mathematics that studies random phenomena. We model simple random experiments mathematically and learn techniques for studying these models. Topics covered include methods of counting (combinatorics), axioms of probability, random variables, the most important discrete and continuous probability distributions, expectations, 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.

Probability theory is ubiquitous in natural science, social science and engineering, so this course can be valuable in conjunction with many different majors. 431 is not a course in statistics. Statistics is a discipline mainly concerned with analyzing and representing data. Probability theory forms the mathematical foundation of statistics, but the two disciplines are separate.

**531 - Probability theory**

The course is a rigorous introduction to probability theory on an advanced undergraduate level. Only a minimal amount of measure theory is used (in particular, Lebesgue integrals will not be needed). The course gives an introduction to the basics (Kolmogorov axioms, conditional probability and independence, random variables, expectation) and discusses some of the classical results of probability theory with proofs (DeMoivre-Laplace limit theorems, the study of simple random walk on Z, applications of generating functions).

This course is currently in development. The pilot version of the course will run in the Spring 2015 semester as 491a - Topics : Probability Theory

632 - Introduction to stochastic processes

635 - Introduction to Brownian motion and stochastic calculus