## ENROLL Course Search

**NOTE:** There are some inconsistencies in the course listing data - ITS is looking into the cause.

**Alternatives:** For requirement lists, please refer to the current catalog. For up-to-the-minute enrollment information, use the "Search for Classes" option in The Hub. If you have any other questions, please email registrar@carleton.edu.

Your search for courses for 22/FA and with code: MATHAPPLIED found 4 courses.

### MATH 240.01 Probability 6 credits

Open: Size: 30, Registered: 20, Waitlist: 0

M | T | W | TH | F |
---|---|---|---|---|

8:30am9:40am | 8:30am9:40am | 8:30am9:30am |

#### Requirements Met:

(Formerly Mathematics 265) Introduction to probability and its applications. Topics include discrete probability, random variables, independence, joint and conditional distributions, expectation, limit laws and properties of common probability distributions.

*Prerequisite:* Mathematics 120 or Mathematics 211

Formerly Mathematics 265

### MATH 240.02 Probability 6 credits

Open: Size: 30, Registered: 29, Waitlist: 0

M | T | W | TH | F |
---|---|---|---|---|

9:50am11:00am | 9:50am11:00am | 9:40am10:40am |

#### Requirements Met:

(Formerly Mathematics 265) Introduction to probability and its applications. Topics include discrete probability, random variables, independence, joint and conditional distributions, expectation, limit laws and properties of common probability distributions.

*Prerequisite:* Mathematics 120 or Mathematics 211

Formerly Mathematics 265

### MATH 241.00 Ordinary Differential Equations 6 credits

Open: Size: 30, Registered: 20, Waitlist: 0

M | T | W | TH | F |
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11:10am12:20pm | 11:10am12:20pm | 12:00pm1:00pm |

#### Requirements Met:

#### Other Tags:

*Prerequisite:* Mathematics 232 or instructor permission

### STAT 340.00 Bayesian Statistics 6 credits

Open: Size: 20, Registered: 18, Waitlist: 0

M | T | W | TH | F |
---|---|---|---|---|

1:50pm3:00pm | 1:50pm3:00pm | 2:20pm3:20pm |

#### Requirements Met:

#### Other Tags:

Formerly MATH 315) An introduction to statistical inference and modeling in the Bayesian paradigm. Topics include Bayes’ Theorem, common prior and posterior distributions, hierarchical models, Markov chain Monte Carlo methods (e.g., the Metropolis-Hastings algorithm and Gibbs sampler) and model adequacy and posterior predictive checks. The course uses R extensively for simulations.

*Prerequisite:* Statistics 250 (formerly Mathematics 275)

Fomerly Mathematics 315

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