統計計算

This course is designed to introduce useful tools in statistical computing. As the speed of computation techniques continuously grow, this course does not follow only one textbook. It is aimed to cover as many of the main tools in statistical computing as possible. Attendants may use any high-level computation software, such as R, SPLUS, SAS, MATLAB, MAPLE, XLISP-STAT, to perform statistical computing. Programs written in low level languages, like C, Python, FORTRAN, together with the other existing computation libraries, such as the R, Python, IMSL libraries, and numerical recipes, are highly encouraged! Undergraduate knowledge of statistical methods is inevitable for understanding the materials.

Statistics

https://misg.stat.nycu.edu.tw/hslu/course/statcomp/index.htm

Course Outline: Simulation and Monte Carlo Methods: Uniform random number generators, bootstrap methods, nonuniform random variate generation, Gibbs samplings, Metropolis-Hastings algorithm, Markov Chain Monte Carlo methods ... Matrix Computation: Least square solutions, Householder transformation, Gram-Schmidt method, Givens rotation, normal equation, Gaussian elimination, singular value decomposition, iterative methods... Symbolic Computation: MAPLE, MATLAB, MATHEMATICA, MACSYMA ... Optimization Methods: Maximum likelihood estimates, simple iteration, bracketing methods, Newton like methods, Fisher scoring methods, the EM algorithm ... Numerical Integration: Monte Carlo integration ... Dynamic Graphics: XLISP-STAT, MATLAB, VISUAL C++ ... Statistical Learning Techniques: Supervised learning, Unsupervised learning... Evaluation: Homework: 70% (Upload them to e3 with your student ID and homework number.) Final Report: 30%

Main References: Ross, S. M. (2022), Simulation, Sixth edition, Academic Press. Gentle, J. E. (2017), Matrix Algebra: Theory, Computations, and Applications in Statistics, Second edition, Springer. Heiberger, R. M., Holland, B. (2015), Statistical Analysis and Data Display: An Intermediate Course with Examples in R, Springer. Tsai, K.-T. (2021), Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction. Chapman and Hall/CRC. Additional References: Golub, G. H., and Van Loan, C. F. (2013), Matrix Computations, Fourth Edition, Johns Hopkins University Press. Thisted, R. A. (1988), Elements of Statistical Computing, Chapman and Hall.

地點

A418

時間

After classes or by appointment.

聯絡方式

Email: henryhslu@nycu.edu.tw Phone: 5731870 or ext 31870