Our goal is to learn as much about probability theory as possible during the quarter. We will focus on the parts of probability theory that are essential to understanding statistics, i.e., the distribution theory of random variables--both exact and large sample theory. The course is also the starting place to learn (more) about stochastic processes which are commonly used as probability models. In addition, stochastic pocess models are the basis for important computational algorithms and a number of prominent types of statistical inference (spatial statistics and the analysis computer experiments).
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(415 Cockins Hall, tjs@stat.osu.edu, Office Hours: MW from 10:00am-11;30am, or by appointment) |
| Thomas Santner |
Grader Jingyuan Yang (Office Hour Thur 2:00pm-3:20pm in 456 Math Building)
Course Logistics and Outline pdf
Course Text Statistical Inference, 2nd Ed by
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George Casella | Roger Berger |
Related Texts on Probability Theory
Course Handouts
Picture Gallery
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| Andrei Kolmogorov (1903 - 1987) | Emile Borel (1871 - 1956) | Chevalier de Mere (1749-1827) |
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| Thomas Bayes (1702 - 1761) | Carl Friedrich Gauss (1871 - 1956) | Ernst Waloddi Weibull (1887 - 1979) |
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| Pavnutii Chebysheff (1821 - 1894) | Augustin-Louis Cauchy (1789-1857) |
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| R.A. Fisher (1890 - 1962) | W.S. Gosset (1876 - 1937) |