Difference between revisions of "Math 431: Probability Theory"

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(Minimal learning outcomes)
(Minimal learning outcomes)
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#** Bernoulli
#** Bernoulli
#** Binomial
#** Binomial
#** Poisson
#** Poisson <br><br><br><br>
# Continuous random variables
# Continuous random variables
#* Probability density function
#* Probability density function

Revision as of 10:32, 18 February 2010

Catalog Information


Probability Theory.

(Credit Hours:Lecture Hours:Lab Hours)





Math 313.


Axiomatic probability theory, conditional probability, discrete / continuous random variables, expectation, conditional expectation, moments, functions of random variables, multivariate distributions, laws of large numbers, central limit theorem.

Desired Learning Outcomes

This course is a calculus-based first course in probability. It is cross-listed with EC En 370.


The current prerequisite is linear algebra. Because of the need to work with joint distributions of continuous random variables in Math 431, the department should consider adding multivariable calculus as a prerequisite.

Minimal learning outcomes

Primarily, students should be able to do basic computation of probabilistic quantities, including those involving applications. Students should be able to recall the most common types of discrete and continuous random variables and describe and compute their properties. Students should understand the theory of probability in an elementary context.

  1. Basic principles of counting
    • Product sets
    • Disjoint unions
    • Combinations
    • Permutations
  2. Axiomatic probability
    • Outcomes
    • Events
    • Probability measures
      • Additivity
      • Continuity
  3. Discrete random variables
    • Probability mass function
    • Cumulative distribution function
    • Moments
      • Expectation
        • Of a function of a random variable
      • Variance
    • Common types
      • Bernoulli
      • Binomial
      • Poisson

  4. Continuous random variables
    • Probability density function
    • Cumulative distribution function
    • Moments
      • Expectation
        • Of a function of a random variable
      • Variance
    • Common types
      • Uniform
      • Exponential
      • Normal
  5. Conditional probability
    • As a probability
    • Bayes' Formula
    • Independence
      • Events
      • Random variables
  6. Joint distributions
    • Covariance
    • Conditional distributions
  7. Conditional expectation
  8. Limit theorems
    • Weak Law of Large Numbers
    • Strong Law of Large Numbers
    • Central Limit Theorem

Additional topics

If time permits, geometric, negative binomial, hypergeometric, gamma, Weibull, Cauchy, and/or beta random variables might be studied.

Courses for which this course is prerequisite

Currently, Math 431 is only a prerequisite for Math 435. Consideration should perhaps be given to making it a prerequisite for Math 543.