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

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MATH 240.01 Probability 6 credits

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

CMC 206

MTWTHF
8:30am9:40am8:30am9:40am8:30am9:30am
Synonym: 61581

Owen D Biesel

(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

CMC 301

MTWTHF
12:30pm1:40pm12:30pm1:40pm1:10pm2:10pm
Synonym: 61582

Owen D Biesel

(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

CMC 210

MTWTHF
12:30pm1:40pm12:30pm1:40pm1:10pm2:10pm
Synonym: 61439

Rob C Thompson

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

CMC 206

MTWTHF
9:50am11:00am9:50am11:00am9:40am10:40am
Synonym: 61454

Adam Loy

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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Requirements
You must take 6 credits of each of these.
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You must take 6 credits of each of these,
except Quantitative Reasoning, which requires 3 courses.
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