Basic Probability:What Every Math Student Should Know

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原   价:
632.00
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474.00
发货周期:预计3-5周发货
作      者
出  版 社
出版时间
2021年07月09日
装      帧
ISBN
9789811237492
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页      码
184 pp
语      种
英文
版      次
2nd ed.
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图书简介
The second edition represents an ongoing effort to make probability accessible to students in a wide range of fields such as mathematics, statistics and data science, engineering, computer science, and business analytics. The book is written for those learning about probability for the first time. Revised and updated, the book is aimed specifically at statistics and data science students who need a solid introduction to the basics of probability. While retaining its focus on basic probability, including Bayesian probability and the interface between probability and computer simulation, this edition?s significant revisions are as follows: ? Many extra motivational examples and problems ? New material on Bayesian probability, including two famous court cases ? New sections on real-world applications of the Poisson distribution ? New sections on generating functions and the bivariate normal density ? New chapter on Markov chains, including Markov chain Monte Carlo simulation The approach followed in the book is to develop probabilistic intuition before diving into details. The best way to learn probability is by practising on a lot of problems. Many instructive problems together with problem-solving strategies are given. Answers to all problems and worked-out solutions to selected problems are also provided. Henk Tijms is the author of several textbooks in the area of applied probability. In 2008, he had received the prestigious INFORMS Expository Writing Award for his work. He is active in popularizing probability at Dutch high schools. Key Features: oThe book uses the approach of probabilistic intuition before getting into details oAn inter-weaved treatment of basic probability and Monte Carlo simulation oA carefully designed collection of motivational examples and problems oThe student is guided in both creative and algorithmic thinking
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