State space models in general, and dynamic linear models in particular, are useful for many types of data and have proven especially popular for time series. After a general introduction to state space models, this course focuses on dynamic linear models, emphasizing their Bayesian analysis. When possible, we show how to calculate estimates and forecasts in closed form; but for more complex models, we use simulation and the dlm package in R. The course includes many detailed examples based on real data sets.

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Valuations, rings of integral elements, ideal theory in algebraic number fields of algebraic functions of one variable, Dirichlet-Hasse unit theorem and Riemann-Roch theorem for curves. Prerequisites: Math 611, 612 or equivalent.

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This course introduces numerical methods used by mechanical engineers. It involves coding algorithms in Matlab and observing the types of numerical errors that arise. It is suitable for junior and senior undergraduates and graduate students. It provides a foundation for Advanced Numerical Analysis (MIE 603), which presumes an understanding of basic numerical methods and focuses on accounting for numerical errors in order to analyze engineering problems using numerical solutions that inherently include error.

This course is structured to deliver a broad understanding of the dynamics in the equine world by introducing equine students to a wide range of professionals within the equine industry (e.g. horse trainers, stable owners, business/investors, researchers, feed companies, and veterinarians). Participation in scheduled lectures given by equine professionals. Lectures will be offered in the evening and will be open to the public. Weekly meetings with the instructor will be required.

Description not available at this time

Not available at this time

Description not available at this time

Practical application of foreign language teaching theories in the classroom. By arrangement with Professor Xiao.