Undergraduate courses in probability: Difference between revisions

From UW-Math Wiki
Jump to navigation Jump to search
VisualWikitext
No edit summary
No edit summary
 
(16 intermediate revisions by 2 users not shown)
Line 1: Line 1:
[https://www.math.wisc.edu/431-introduction-theory-probability '''431 - Introduction to the theory of probability''']
'''331 - Introductory Probability'''
 
Math 331 is an introductory probability course without a multivariate calculus prerequisite. 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. The course also includes a brief introduction to techniques of multivariate integration.  
 
Note that the course does not count towards the requirements of the math major, but it does count towards the math certificate.  
 
The course is offered every fall and spring semester.
 
''Prerequisite'': Math 213 or Math 222.
 
<span style="color:#0000FF"> '''Who should take this class?'''</span> Students who are interested to get an introduction to probability, but do not intend to take multivariate calculus (Math 234 or Math 375).
 
 
'''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.
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.
Line 7: Line 20:
The course is offered every semester, including the summer.  
The course is offered every semester, including the summer.  


''Prerequisite'': Math 234.
''Prerequisite'': Math 234 or Math 375


<span style="color:#0000FF"> '''Who should take this class?'''</span> A well rounded undergraduate experience in math should include some probability theory. Math 431 is our introductory probability class with no high level prerequisites.  
<span style="color:#0000FF"> '''Who should take this class?'''</span> A well rounded undergraduate experience in math should include some probability theory. Math 431 is our introductory probability class with no high level prerequisites.  




Line 18: Line 30:
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).
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 [https://www.math.wisc.edu/491a-topics-probability-theory 491a - Topics : Probability Theory].  
The course is offered every spring.


''Prerequisite'': a proof based analysis course (Math 375, Math 421 or Math 521).  
''Prerequisite'': a proof based analysis course (Math 376, Math 421 or Math 521).  


<span style="color:#0000FF"> '''Who should take this class?'''</span> Students who would like to get a rigorous introduction to probability. It could also provide a stepping stone for our 600 level stochastic processes courses. (The course can be taken even after taking Math 431.)


[http://www.math.wisc.edu/math-605stochastic-methods-biology '''605 - Stochastic methods in biology''']


This course is, first and foremost, an introduction to stochastic processes (models that incorporate randomness) that is equivalent in level, and to a large extent content, to Math 632.  However, the applications will be drawn primarily from the biosciences, with a special emphasis on the continuous time Markov chains used to model biochemical and other population processes.  Further, as simulation is a large part of how scientists study their models, we will spend considerable time on these methods (such as the well known ``Gillespie Algorithm'').  Matlab will be the software package of choice for the course and each homework assignment will incorporate at least one Matlab exercise.


The course is offered every two years in the fall semester.


''Prerequisite'': Math 431, a basic knowledge of linear algebra and linear differential equations (e.g. Math 319, Math 340, Math 341)


'''632 - Introduction to stochastic processes'''


[https://www.math.wisc.edu/632-introduction-stochastic-processes '''632 - Introduction to stochastic processes''']
Math 632 gives an introduction to Markov chains and Markov processes with discrete state spaces and their applications. Particular models studied include birth-death chains, queuing models, random walks and branching processes. Selected topics from renewal theory, martingales, and Brownian motion are also included, but vary from semester to semester to meet the needs of different audiences. 


Math 632 gives an introduction to Markov chains and Markov processes with discrete state spaces and their applications. Particular models studied include birth-death chains, queuing models, random walks and branching processes. Selected topics from renewal theory, martingales, and Brownian motion are also included, but vary from semester to semester to meet the needs of different audiences. 
''Prerequisite'': Intro to probability (Math 309, 431 or 531)+ a linear algebra or an intro to proofs class (320, 340, 341, 375, 421)


''Prerequisite'': Math 431, a high level math course (e.g. Math 521)
<span style="color:#0000FF"> '''Who should take this class?'''</span>  Math 632 is the natural next step after an introductory probability course. It could be useful for a math major interested in higher level probability and it is also a great fit for many of our [https://guide.wisc.edu/undergraduate/letters-science/mathematics/mathematics-ba/#requirementstext named options in math].






[https://www.math.wisc.edu/635-introduction-brownian-motion-and-stochastic-calculus '''635 - Introduction to Brownian motion and stochastic calculus''']
'''635 - Introduction to Brownian motion and stochastic calculus'''


Math 635 is an introduction to Brownian motion and stochastic calculus without a measure theory prerequisite. Topics touched upon include sample path properties of Brownian motion, Itô stochastic integrals, Itô's formula, stochastic differential equations and their solutions. As an application we will discuss the Black-Scholes formula of mathematical finance.
Math 635 is an introduction to Brownian motion and stochastic calculus without a measure theory prerequisite. Topics touched upon include sample path properties of Brownian motion, Itô stochastic integrals, Itô's formula, stochastic differential equations and their solutions. As an application we will discuss the Black-Scholes formula of mathematical finance.
Line 47: Line 57:


''Prerequisite'': Math 521 and Math 632
''Prerequisite'': Math 521 and Math 632
<span style="color:#0000FF"> '''Who should take this class?'''</span> Anybody with an interest in higher level probability. It is especially useful for those who are planning to study financial math on a graduate level.




[[File:Probability courses.jpg]]
<!--[[File:Probability_courses_1.jpg|600px]]-->

Latest revision as of 20:02, 10 September 2024

331 - Introductory Probability

Math 331 is an introductory probability course without a multivariate calculus prerequisite. 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. The course also includes a brief introduction to techniques of multivariate integration.

Note that the course does not count towards the requirements of the math major, but it does count towards the math certificate.

The course is offered every fall and spring semester.

Prerequisite: Math 213 or Math 222.

Who should take this class? Students who are interested to get an introduction to probability, but do not intend to take multivariate calculus (Math 234 or Math 375).


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.

The course is offered every semester, including the summer.

Prerequisite: Math 234 or Math 375

Who should take this class? A well rounded undergraduate experience in math should include some probability theory. Math 431 is our introductory probability class with no high level prerequisites.


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).

The course is offered every spring.

Prerequisite: a proof based analysis course (Math 376, Math 421 or Math 521).

Who should take this class? Students who would like to get a rigorous introduction to probability. It could also provide a stepping stone for our 600 level stochastic processes courses. (The course can be taken even after taking Math 431.)



632 - Introduction to stochastic processes

Math 632 gives an introduction to Markov chains and Markov processes with discrete state spaces and their applications. Particular models studied include birth-death chains, queuing models, random walks and branching processes. Selected topics from renewal theory, martingales, and Brownian motion are also included, but vary from semester to semester to meet the needs of different audiences.

Prerequisite: Intro to probability (Math 309, 431 or 531)+ a linear algebra or an intro to proofs class (320, 340, 341, 375, 421)

Who should take this class? Math 632 is the natural next step after an introductory probability course. It could be useful for a math major interested in higher level probability and it is also a great fit for many of our named options in math.


635 - Introduction to Brownian motion and stochastic calculus

Math 635 is an introduction to Brownian motion and stochastic calculus without a measure theory prerequisite. Topics touched upon include sample path properties of Brownian motion, Itô stochastic integrals, Itô's formula, stochastic differential equations and their solutions. As an application we will discuss the Black-Scholes formula of mathematical finance.

The course is offered every two years in the spring semester.

Prerequisite: Math 521 and Math 632

Who should take this class? Anybody with an interest in higher level probability. It is especially useful for those who are planning to study financial math on a graduate level.


Retrieved from "https://wiki.math.wisc.edu/index.php?title=Undergraduate_courses_in_probability&oldid=27057"