Pt. I, Preliminaries. Mathematical and statistical preliminaries -- Pt. II, Statistical computing. Computer storage and arithmetic ; algorithm and programming ; Approximation of functions and numerical quadrature ; Numerical linear algebra ; Solution of nonlinear equations and optimization ; Generation of random numbers -- Pt. III, Methods of computational statistics. Graphical methods in computational statistics ; Tools for identification of structure in data ; Estimation of functions ; Monte Carlo methods for statistical inference ; Data randomization, partitioning, and augmentation ; Bootstrap methods -- Pt. IV, Exploring data density and relationships. Estimation of probability density functions using parametric models ; Nonparametric estimation of probability density functions ; Statistical learning and data mining ; Statistical models of dependencies.
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