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Probabilities --- Data processing --- Data processing. --- Probabilités. --- Probabilities - Data processing --- Probabilités.

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Mathematical statistics --- Probabilities --- BASIC (Computer program language) --- Data processing --- -Probabilities --- -10.01.a --- 10.02.b --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Risk --- Statistics, Mathematical --- Statistics --- Sampling (Statistics) --- Beginner's All-Purpose Symbolic Instruction Code (Computer program language) --- Programming languages (Electronic computers) --- Verzekeringswiskunde ; Waarschijnlijkheidsrekening --- Statistieken ; Wiskundige statistiek --- Statistical methods --- Probabilities - Data processing --- Mathematical statistics - Data processing

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Information systems --- Mathematical statistics --- Probabilities --- Statistics --- R (Computer program language) --- Probabilités --- Statistique --- R (Langage de programmation) --- Data processing --- Informatique --- 519.2 --- Probability. Mathematical statistics --- 519.2 Probability. Mathematical statistics --- Probabilités --- GNU-S (Computer program language) --- Domain-specific programming languages --- Probabilities - Data processing --- Statistics - Data processing --- Informatiques -- langage de programmation r = informatics -- r computer program language

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Statistics --- Probabilities --- Electronic spreadsheets --- Data processing --- Collecte de données --- data collection --- Traitement des données --- Analyse de données --- Data analysis --- Application des ordinateurs --- computer applications --- 519.2 --- -Statistics --- -311 --- 519 --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Electronic spread sheets --- Spread sheets, Electronic --- Spreadsheeting, Electronic --- Spreadsheets, Electronic --- Business --- Probability. Mathematical statistics --- Statistiekwetenschap --- Kanstheorie. Waarschijnlijkheidsrekening --- Kansrekening --- Toegepaste statistiek --- Electronic spreadsheets. --- Data processing. --- Use of --- Computers --- Kansrekening. --- Toegepaste statistiek. --- Computers. --- 519.2 Probability. Mathematical statistics --- 311 --- Probabilités --- Statistiques --- Tableurs --- Informatique. --- Statistics - Data processing --- Probabilities - Data processing --- Probabilités

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Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods. Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally-intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods. The book assumes an intermediate background in mathematics, computing, and applied and theoretical statistics. The first part of the book, consisting of a single long chapter, reviews this background material while introducing computationally-intensive exploratory data analysis and computational inference. The six chapters in the second part of the book are on statistical computing. This part describes arithmetic in digital computers and how the nature of digital computations affects algorithms used in statistical methods. Building on the first chapters on numerical computations and algorithm design, the following chapters cover the main areas of statistical numerical analysis, that is, approximation of functions, numerical quadrature, numerical linear algebra, solution of nonlinear equations, optimization, and random number generation. The third and fourth parts of the book cover methods of computational statistics, including Monte Carlo methods, randomization and cross validation, the bootstrap, probability density estimation, and statistical learning. The book includes a large number of exercises with some solutions provided in an appendix. James E. Gentle is University Professor of Computational Statistics at George Mason University. He is a Fellow of the American Statistical Association (ASA) and of the American Association for the Advancement of Science. He has held several national offices in the ASA and has served as associate editor of journals of the ASA as well as for other journals in statistics and computing. He is author of Random Number Generation and Monte Carlo Methods and Matrix Algebra.

Mathematical statistics -- Congresses. --- Mathematical statistics -- Data processing. --- Mathematical statistics. --- Probabilities -- Data processing. --- Mathematical statistics --- Mathematical Statistics --- Mathematics --- Physical Sciences & Mathematics --- Data processing --- Statistics --- Data processing. --- Mathematics. --- Computer science --- Numerical analysis. --- Data mining. --- Computer mathematics. --- Probabilities. --- Statistics. --- Probability Theory and Stochastic Processes. --- Computational Mathematics and Numerical Analysis. --- Mathematics of Computing. --- Statistics and Computing/Statistics Programs. --- Numeric Computing. --- Data Mining and Knowledge Discovery. --- Distribution (Probability theory. --- Computer science. --- Electronic data processing. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- ADP (Data processing) --- Automatic data processing --- EDP (Data processing) --- IDP (Data processing) --- Integrated data processing --- Computers --- Office practice --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Informatics --- Science --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- Distribution functions --- Frequency distribution --- Characteristic functions --- Automation --- Statistical methods --- Computer science—Mathematics. --- Statistics . --- Mathematical analysis --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Probability --- Combinations --- Chance --- Least squares --- Risk --- Mathematical statistics - Data processing