Duke University 2009-2010 - Office of the Registrar - Duke University

factors and intrinsic Bayes factors for linear models, and Bayesian model averaging. The concepts of linear models from
Bayesian and classical viewpoints. Topics in Markov chain Monte Carlo simulation introduced as required.
Prerequisite: Statistics 213 and 290 or equivalent. Instructor: Clyde. 3 units. C-L: Mathematics 217
250. Numerical Analysis. 3 units. C-L: see Computer Science 250; also C-L: Mathematics 221
253. Applied Stochastic Processes. 3 units. C-L: see Mathematics 216
270. Statistical Methods for Computational Biology. 3 units. C-L: see Computational Biology and Bioinformatics 240
271. Statistical Genetics. 3 units. C-L: see Computational Biology and Bioinformatics 241
277. Computational Structural Biology. 3 units. C-L: see Computer Science 263B; also C-L: Computational Biology
and Bioinformatics 250
278. Computational Gene Expression Analysis. 1 unit. C-L: see Computational Biology and Bioinformatics 221; also
C-L: Molec Genetics & Microbiology 221
280. Spatial Statistics. Modeling data with spatial structure;point-referenced (geo-statistical)data, areal (lattice) data,
and point process data; stationarity, valid covariance functions; Gaussian processes and generalizations; kriging;
Markov random fields (CAR and SAR); hierarchical modeling for spatial data; misalignment; multivariate spatial data,
space/time data specification. Theory and application. Some assignments will involve computing and data analysis.
Consent of instructor required. Instructor: Gelfand. 3 units.
281. Modern Nonparametric Theory and Methods. Modern nonparametric approaches for exploring and drawing
inferences from data. Topics may include: resampling methods, nonparametric density estimation, nonparametric
regression and classification, bootstrapping, kernel methods, splines, local regression, wavelets, support vector
machines, nonparametric modeling for random distributions. Classical and Bayesian perspectives. Consent of instructor
required. Instructor: Dunson. 3 units.
290. Modern Statistical Data Analysis. Introduction to statistical thinking, data management and collection, sampling
and design, exploratory data analysis, graphical and tabular displays, summarizing data. Introduction to applied work.
Computer orientation, statistical packages and operating systems, especially unix on high-speed workstations, and the
statistical package S-Plus. Graphics and numerical computing. Examples from various disciplines. Instructor: Clyde.
3 units.
291. Independent Study. Directed reading and research. Consent of instructor and director of graduate studies required.
Instructor: Staff. Variable credit.
292. Independent Study. Directed reading and research. Consent of instructor and director of graduate studies required.
Instructor: Staff. Variable credit.
293. Special Topics in Statistics. Prerequisite: Statistics 213 or consent of instructor. Pass/Fail grading only. Instructor:
Staff. 3 units.
294. Special Topics in Statistics. Prerequisite: Statistics 213 or consent of instructor. Pass/Fail grading only. Instructor:
Staff. 3 units.
294A. Special Topics in Statistics. Prerequisite: Statistics 213 or consent of instructor. Credit/Non-Credit grading only.
Instructor: Staff. 2 units.
295. First-Year Seminar. Weekly seminar covering a variety of statistical subjects. Coregistration in Statistics 213 and
Statistics 244 or consent of instructor. Instructor: Staff. Variable credit.
297. Topics in Probability Theory. 3 units. C-L: see Mathematics 288
356. Time Series and Forecasting. Time series data and models: trend, seasonality, and regressions. Traditional models:
EWMA, EWR, ARMA. Dynamic linear models (DLMs). Bayesian learning, forecasting, and smoothing. Mathematical
structure of DLMs and related models. Intervention, forecast monitoring, and control. Structural change in time series.
Multiprocess models and mixture analysis. Multivariate models, constrained and aggregate forecasting, and forecast
combination. Applications using computer software. Other topics, including spectral analysis, as time permits.
Prerequisite: Statistics 215 or equivalent. Instructor: West. 3 units.
357. Stochastic Processes. Conditional probabilities and Radon-Nikodym derivatives of measures; tightness and weak
convergence of probability measures, measurability and observability. Markov chains, Brownian motion, Poisson
processes. Gaussian processes, birth-and-death processes, and an introduction to continuous-time martingales.
Prerequisite: Statistics 205 (or Mathematics 290) and Statistics 215 (or Mathematics 136.) Instructor: Wolpert. 3 units.
376. Advanced Modeling and Scientific Computing. An introduction to advanced statistical modeling and modern
numerical methods useful in implementing statistical procedures for data analysis, model exploration, inference, and
prediction. Topics include simulation techniques for maximization and integration. Prerequisite: Computer Science 221
or equivalent. Instructor: West. 3 units.
390. Statistical Consulting Workshop. Under faculty supervision, students address and solve consulting problems
submitted to ISDS's campus-wide consulting program, and present their solutions to the class. May be taken more than
once. Consent of instructor required. Instructor: Staff. 1 unit.
Departments, Programs, and Course Offerings 222