## ENROLL Course Search

Your search for courses for 21/FA and with code: MATHAPPLIED found 4 courses.

### MATH 240.01 Probability 6 credits

Closed: Size: 25, Registered: 29, Waitlist: 0

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

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(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

Closed: Size: 25, Registered: 32, Waitlist: 0

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12:30pm1:40pm | 12:30pm1:40pm | 1:10pm2:10pm |

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(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 271.00 Computational Mathematics 6 credits

Open: Size: 25, Registered: 17, Waitlist: 0

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12:30pm1:40pm | 12:30pm1:40pm | 1:10pm2:10pm |

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An introduction to mathematical ideas from numerical approximation, scientific computing, and/or data analysis. Topics will be selected from numerical linear algebra, numerical analysis, and optimization. Theory, implementation, and application of computational methods will be emphasized.

*Prerequisite:* Mathematics 232

Not open to students who have already received credit for Mathematics 295 Numerical Analysis

### STAT 340.00 Bayesian Statistics 6 credits

Closed: Size: 20, Registered: 25, Waitlist: 0

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

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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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