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Statistical Computing, as a book category, covers the theory, methods, algorithms, and practical tools used to perform statistical analysis by computation rather than by purely analytic derivation. Titles in this category explain numerical and algorithmic approaches (e.g., optimization, Monte Carlo and MCMC, EM, bootstrapping, numerical linear algebra), scalable and high-performance techniques for large datasets, probabilistic simulation, and software engineering practices for reproducible data analysis and statistical software development. They typically mix mathematical foundations with hands-on examples and code in languages such as R, Python, Julia, or C/C++, and address topics like visualization, model-fitting, Bayesian computation, parallel and distributed computing, and performance tuning. Intended for statisticians, data scientists, and computational researchers, books range from introductory cookbooks and tutorials to advanced texts on algorithm design and implementation for real-world statistical problems.

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