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| CSI 972 - Mathematical Statistics I |
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Focuses on theory of estimation, exploring method of moments, least squares, maximum likelihood, and maximum entropy methods. Details methods of minimum variance unbiased estimation. Other topics include sufficiency and completeness of statistics, Fisher information, Cramer-Rao bounds, Bhattacharyya bounds, asymptotic consistency and distributions, statistical decision theory, minimax and Bayesian decision rules, and applications to engineering and scientific problems.
3.000 Credit hours 3.000 Lecture hours Levels: Graduate Schedule Types: Lecture Computational & Data Sciences Department Course Attributes: Graduate - Advanced Restrictions: Must be enrolled in one of the following Levels: Graduate Prerequisites: (Graduate level CSI 672 Minimum Grade of B- or Graduate level STAT 652 Minimum Grade of B-) and (Graduate level CSI 876 Minimum Grade of B- or Graduate level IT 876 Minimum Grade of B- or Graduate level STAT 876 Minimum Grade of B- or Graduate level IT 971 Minimum Grade of B- or Graduate level STAT 971 Minimum Grade of B-) |
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