Probability and statistics for data science : math + R + data

• 作者： Matloff, Norman S., author.
• 其他題名：
  • Series in computer science and data analysis.
• 出版： Boca Raton : CRC Press, Taylor & Francis Group
• 叢書名： Chapman & Hall/CRC data science series
• 主題： Probabilities--Textbooks. , Mathematical statistics--Textbooks. , Probabilities--Data processing. , Mathematical statistics--Data processing. , Textbooks.
• ISBN： 9781138393295 (pbk.): GBP52.99 、 1138393290 (pbk.) 、 9780367260934 (hbk.) 、 036726093X (hbk.)
• 資料類型: 圖書
• 內容註: Contains bibliographical references (pages 391-394) and index. Basic probability models -- Monte Carlo simulation -- Discrete random variables: expected value -- Discrete random variables: variance -- Discrete parametric distribution families -- Continuous probability models -- Statistics: prologue -- Fitting continuous models -- The family of normal distributions -- Introduction to statistical inference -- Multivariate distributions -- The multivariate normal family of distributions -- Mixture distributions -- Multivariate description and dimension reduction -- Predictive modeling -- Model parsimony and overfitting -- Introduction to discrete time Markov chains -- Appendices: A. R Quick Start -- B. Matrix algebra.
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• 摘要註: "Probability and Statistics for Data Science: Math + R + Data covers "math stat"--distributions, expected value, estimation etc.--but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming." --Amazon.com.
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