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STAT 458: Math Modeling in Life Sciences
3.00 - 4.00 Credits
University of Illinois at Urbana-Champaign
Same as ANSC 448 and IB 487. See ANSC 448.
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STAT 458 - Math Modeling in Life Sciences
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STAT 466: Image and Neuroimage Analysis
3.00 - 4.00 Credits
University of Illinois at Urbana-Champaign
Same as BIOE 466 and PSYC 466. See PSYC 466.
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STAT 466 - Image and Neuroimage Analysis
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STAT 510: Mathematical Statistics I
4.00 Credits
University of Illinois at Urbana-Champaign
Distributions, transformations, order-statistics, exponential families, sufficiency, delta-method, Edgeworth expansions; uniformly minimum variance unbiased estimators, Rao-Blackwell theorem, Cramer-Rao lower bound, information inequality; equivariance. Prerequisite: STAT 410.
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STAT 510 - Mathematical Statistics I
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STAT 511: Mathematical Statistics II
4.00 Credits
University of Illinois at Urbana-Champaign
Bayes estimates, minimaxity, admissibility; maximum likelihood estimation, consistency, asymptotic efficiency; testing and confidence intervals; Neyman-Pearson lemma, uniformly most powerful tests; likelihood ratio tests and large-sample approximation; nonparametrics. Prerequisite: STAT 510.
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STAT 511 - Mathematical Statistics II
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STAT 525: Computational Statistics
4.00 Credits
University of Illinois at Urbana-Champaign
Various topics, such as ridge regression; robust regression; jackknife, bootstrap, cross-validation and resampling plans; E-M algorithm; projection pursuit; all with a strong computational flavor. May be repeated if topics vary. Prerequisite: STAT 425, STAT 426, and STAT 511; or consent of instructor.
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STAT 525 - Computational Statistics
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STAT 530: Bioinformatics
4.00 Credits
University of Illinois at Urbana-Champaign
Same as ANSC 543, CHBE 571, and MCB 571. See CHBE 571.
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STAT 530 - Bioinformatics
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STAT 542: Statistical Learning
4.00 Credits
University of Illinois at Urbana-Champaign
Modern techniques of predictive modeling, classification, and clustering are discussed. Examples of these are linear regression, nonparametric regression, kernel methods, regularization, cluster analysis, classification trees, neural networks, boosting, discrimination, support vector machines, and model selection. Applications are discussed as well as computation and theory. Prerequisite: STAT 410 and STAT 425.
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STAT 542 - Statistical Learning
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STAT 551: Theory of Probability I
4.00 Credits
University of Illinois at Urbana-Champaign
Same as MATH 561. See MATH 561.
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STAT 551 - Theory of Probability I
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STAT 552: Theory of Probability II
4.00 Credits
University of Illinois at Urbana-Champaign
Same as MATH 562. See MATH 562.
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STAT 552 - Theory of Probability II
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STAT 553: Probability and Measure I
4.00 Credits
University of Illinois at Urbana-Champaign
Measures and probabilities; integration and expectation; convergence theorems and inequalities for integrals and expectations; independence; convergence in probability, almost surely, and mean; Three Series Theorem; laws of large numbers. Credit is not given for both STAT 553 and either MATH 540 or MATH 561. Prerequisite: MATH 447 or consent of instructor.
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STAT 553 - Probability and Measure I
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