3.0

credits

Average Course Rating

Covers the theory and application of common algorithms used in statistical computing. Includes topics: root finding, optimization, numerical integration, Monte Carlo, Markov chain Monte Carlo, stochastic optimization, and bootstrapping. Discusses specific algorithms: Newton-Raphson, EM, Metropolis-Hastings algorithm, Gibbs sampling, simulated annealing, Gaussian quadrature, Romberg integration, etc. Discusses applications of these algorithms to real research problems.