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PHP 2690F - Advanced Topics in Statistics: Statistical Computing |
Covers the theory and application of common algorithms used in statistical computing including numerical analysis, random number generation, sorting, root finding, optimization, numerical integration, simulation and Monte Carlo methods, smoothing and density estimation, Markov chain Monte Carlo and bootstrapping. Some specific topics discussed include: rejection sampling, Newton-Raphson, Sweep, Gaussian quadrature, EM, importance sampling, Metropolis-Hastings, Gibbs sampling, kernel densities, maximum likelihood, simplex algorithm, etc. Necessary numerical linear algebra and analysis will be reviewed. Also discusses applications of these algorithms to real research problems. Recommended course work in multivariable calculus, linear algebra, and statistics (PHP 2510, 2511).
1.000 Credit hours 1.000 Lecture hours Levels: Graduate, Undergraduate Schedule Types: Primary Meeting Public Health Department |
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