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Production management --- statistiek --- kwaliteitscontrole --- Statistical science --- Mathematical statistics --- -304.8 --- Quality control --- Sampling (Statistics) --- 658.562 --- Bedrijfsstatistiek --- Kwaliteitszorg --- #TWER:BOEK --- 301 --- 302 --- 304.6 --- 304.8 --- AA / International- internationaal --- Random sampling --- Statistics of sampling --- Statistics --- 658.562 Control of operation. Production supervision. Quality control and inspection --- Control of operation. Production supervision. Quality control and inspection --- Statistical methods --- Techniek van statistische inlichtingen. Organisatie van de statistische enquêtes. Statistische kritiek --- Opmaak en presentatie van statistische reeksen en tabellen. Grafieken --- Variantie-analyse, tests --- Steekproeftheorie --- Sampling (Statistics). --- Statistical methods. --- Industrial statistics. --- Échantillonnage (statistique) --- Statistiques industrielles. --- Qualité --- Contrôle. --- Quality control - Statistical methods --- Échantillonnage (statistique) --- Qualité --- Contrôle.

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This book offers a comprehensive guide to large sample techniques in statistics. With a focus on developing analytical skills and understanding motivation, Large Sample Techniques for Statistics begins with fundamental techniques, and connects theory and applications in engaging ways. The first five chapters review some of the basic techniques, such as the fundamental epsilon-delta arguments, Taylor expansion, different types of convergence, and inequalities. The next five chapters discuss limit theorems in specific situations of observational data. Each of the first ten chapters contains at least one section of case study. The last six chapters are devoted to special areas of applications. This new edition introduces a final chapter dedicated to random matrix theory, as well as expanded treatment of inequalities and mixed effects models. The book's case studies and applications-oriented chapters demonstrate how to use methods developed from large sample theory in real world situations. The book is supplemented by a large number of exercises, giving readers opportunity to practice what they have learned. Appendices provide context for matrix algebra and mathematical statistics. The Second Edition seeks to address new challenges in data science. This text is intended for a wide audience, ranging from senior undergraduate students to researchers with doctorates. A first course in mathematical statistics and a course in calculus are prerequisites.

Statistical science --- Operational research. Game theory --- Probability theory --- waarschijnlijkheidstheorie --- stochastische analyse --- statistiek --- kansrekening --- statistisch onderzoek --- Mathematical statistics. --- Sampling (Statistics) --- Mostreig (Estadística)

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Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine learning --- Probabilities --- Apprentissage automatique --- Probabilités --- Machine Learning --- Machine learning. --- Probabilities. --- Basic Sciences. Statistics --- Probability Theory, Sampling Theory --- Artificial intelligence. Robotics. Simulation. Graphics --- Probability Theory, Sampling Theory. --- Probabilités --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk --- Learning, Machine --- Artificial intelligence --- Machine theory --- Apprentissage automatique. --- Probabilités. --- machine learning --- KI (kunstmatige intelligentie) --- Aprendizaje automático --- Probabilidades --- Libros electrónicos --- Probabilités. --- AI (artificiële intelligentie)

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Probabilities --- Mathematical statistics --- Probabilités --- Statistique mathématique --- Periodicals. --- Périodiques --- Mathematical statistics. --- Probabilities. --- Mathematical Sciences --- Applied Mathematics --- probability --- statistics --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Risk --- Statistics, Mathematical --- Statistics --- Sampling (Statistics) --- Statistical methods --- Probability theory --- Mathematical Statistics

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smart organisations --- smart society --- product quality --- service quality --- open innovation --- quality management --- Business policy --- Quality control --- Factory management --- Industrial engineering --- Reliability (Engineering) --- Sampling (Statistics) --- Standardization --- Quality assurance --- Quality of products --- Industrial & Management Engineering