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111 - 119 of 119 results for: STATS

STATS 363: Design of Experiments (STATS 263)

Experiments vs observation. Confounding. Randomization. ANOVA.Blocking. Latin squares. Factorials and fractional factorials. Split plot. Response surfaces. Mixture designs. Optimal design. Central composite. Box-Behnken. Taguchi methods. Computer experiments and space filling designs. Prerequisites: probability at STATS 116 level or higher, and at least one course in linear models.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Owen, A. (PI)

STATS 366: Modern Statistics for Modern Biology (BIOS 221)

Application based course in nonparametric statistics. Modern toolbox of visualization and statistical methods for the analysis of data, examples drawn from immunology, microbiology, cancer research and ecology. Methods covered include multivariate methods (PCA and extensions), sparse representations (trees, networks, contingency tables) as well as nonparametric testing (Bootstrap, permutation and Monte Carlo methods). Hands on, use R and cover many Bioconductor packages. Prerequisite: Minimal familiarity with computers. Instructor consent.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Holmes, S. (PI)

STATS 374: Large Deviations Theory (MATH 234)

Combinatorial estimates and the method of types. Large deviation probabilities for partial sums and for empirical distributions, Cramer's and Sanov's theorems and their Markov extensions. Applications in statistics, information theory, and statistical mechanics. Prerequisite: MATH 230A or STATS 310. Offered every 2-3 years.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

STATS 375: Inference in Graphical Models

Graphical models as a unifying framework for describing the statistical relationships between large sets of variables; computing the marginal distribution of one or a few such variables. Focus is on sparse graphical structures, low-complexity algorithms, and their analysis. Topics include: variational inference; message passing algorithms; belief propagation; generalized belief propagation; survey propagation. Analysis techniques: correlation decay; distributional recursions. Applications from engineering, computer science, and statistics. Prerequisite: EE 278, STATS 116, or CS 228. Recommended: EE 376A or STATS 217.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

STATS 390: Consulting Workshop

Skills required of practicing statistical consultants, including exposure to statistical applications. Students participate as consultants in the department's drop-in consulting service, analyze client data, and prepare formal written reports. Seminar provides supervised experience in short term consulting. May be repeated for credit. Prerequisites: course work in applied statistics or data analysis, and consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-3 | Repeatable for credit | Grading: Satisfactory/No Credit

STATS 397: PhD Oral Exam Workshop

For Statistics PhD students defending their dissertation.
Terms: Spr | Units: 1 | Grading: Satisfactory/No Credit

STATS 398: Industrial Research for Statisticians

Doctoral research as in 298, but must be conducted for an off-campus employer. Final report required. May be repeated for credit. Prerequisite: Statistics Ph.D. candidate.
Terms: Aut, Win, Spr, Sum | Units: 1-3 | Repeatable for credit | Grading: Letter or Credit/No Credit

STATS 801: TGR Project

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

STATS 802: TGR Dissertation

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR
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