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Authors: Nitis Mukhopadhyay (2000) - This textbook reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, numerous figures and tables, and computer simulations to develop and illustrate concepts - reinforcing important ideas and emphasizing special techniques with drills and boxed summaries
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Authors: David G. Luenberger (2016) - This new edition covers the central concepts of practical optimization techniques, with an emphasis on methods that are both state-of-the-art and popular. Again a connection between the purely analytical character of an optimization problem and the behavior of algorithms used to solve the problem. As in the earlier editions, the material in this fourth edition is organized into three separate parts. Part I is a self-contained introduction to linear programming covering numerical algorithms and many of its important special applications. Part II, which is independent of Part I, covers the theory of unconstrained optimization, including both derivations of the appropriate optimality conditions and an introduction to basic algorithms. Part III extends the concepts developed in the seco...
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Authors: Đào Hữu Hồ (2007) - Một số khái niệm, kết quả cơ bản của xác suất và thống kê xã hội được trình bày qua các bài toán giải tích tổ hợp, phép thử và biến cố, biến ngẫu nhiên, hàm phân phối, các số đặc trưng của biến ngẫu nhiên, lí thuyết mẫu, ước lượng đơn giản, bài toán kiểm định giả thiết đơn giản, tương quan và hồi qui..
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Authors: Y. Suhov; M. Kelbert (2005) - Probability and Statistics are as much about intuition and problem solving, as they are about theorem proving. Because of this, students can find it very difficult to make a successful transition from lectures to examinations to practice, since the problems involved can vary so much in nature.
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Authors: Mark Zegarelli (2022) - Offers explanations of concepts such as whole numbers, fractions, decimals, and percents, and covers advanced topics including imaginary numbers, variables, and algebraic equations
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- Sách/Book
Authors: John Tabak (2004) - A primer on probability and statistics that includes a chronology of notable events, a glossary of terms, and an array of sources for further research
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Authors: Morris H. DeGroot (1986) - The revision of this well-respected text presents a balance of the classical and Bayesian methods. The theoretical and practical sides of both probability and statistics are considered. New content areas include the Vorel- Kolmogorov Paradox, Confidence Bands for the Regression Line, the Correction for Continuity, and the Delta Method.
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Authors: Dinh The Luc (2016) - This book introduces the reader to the field of multiobjective optimization through problems with simple structures, namely those in which the objective function and constraints are linear. Fundamental notions as well as state-of-the-art advances are presented in a comprehensive way and illustrated with the help of numerous examples. Three of the most popular methods for solving multiobjective linear problems are explained, and exercises are provided at the end of each chapter, helping students to grasp and apply key concepts and methods to more complex problems. The book was motivated by the fact that the majority of the practical problems we encounter in management science, engineering or operations research involve conflicting criteria and therefore it is more convenient to formu...
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Authors: Trần Vũ Thiệu (2011) - Lý thuyết chung về bài toán tối ưu, giải tích lồi, điều kiện tối ưu, bài toán ngẫu hứng. Phương pháp tìm cực tiểu không ràng buộc và có ràng buộc, phương pháp không dùng đạo hàm, phương pháp gradient, phương pháp tuyến tính hoá..
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Authors: Kevin P. Murphy (2022) - "This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR"
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