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Titlebook: Probability for Physicists; Simon Sirca Textbook 2016 Springer International Publishing Switzerland 2016 Entropy of physical systems.Gener

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發(fā)表于 2025-3-21 19:19:14 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Probability for Physicists
編輯Simon Sirca
視頻videohttp://file.papertrans.cn/757/756947/756947.mp4
概述Gives a quick and intuitive introduction to the key concepts of probability, statistics and randomness.Covers the basics of entropy, Markov processes, Monte-Carlo methods and stochastic population mod
叢書(shū)名稱Graduate Texts in Physics
圖書(shū)封面Titlebook: Probability for Physicists;  Simon Sirca Textbook 2016 Springer International Publishing Switzerland 2016 Entropy of physical systems.Gener
描述This book is designed as a practical and intuitive introduction to probability, statistics and random quantities for physicists. The book aims at getting to the main points by a clear, hands-on exposition supported by well-illustrated and worked-out examples. A strong focus on applications in physics and other natural sciences is maintained throughout. In addition to basic concepts of random variables, distributions, expected values and statistics, the book discusses the notions of entropy, Markov processes, and fundamentals of random number generation and Monte-Carlo methods.
出版日期Textbook 2016
關(guān)鍵詞Entropy of physical systems; Generation of pseudorandom numbers; Markov processes; Monte Carlo method; P
版次1
doihttps://doi.org/10.1007/978-3-319-31611-6
isbn_softcover978-3-319-81078-2
isbn_ebook978-3-319-31611-6Series ISSN 1868-4513 Series E-ISSN 1868-4521
issn_series 1868-4513
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

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Probability Distributionscontinuous and discrete random variables and their corresponding probability distributions. Probability functions, probability densities and (cumulative) distribution functions are introduced. Transformations of random variables are discussed, with particular attention given to the cases where the i
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Special Continuous Probability Distributionsterizing the decay of unstable atoms and nuclei, the ubiquitous normal (Gauss) distribution in both its general and standardized form, the Maxwell velocity distribution in its vector and scalar form, the Pareto (power-law) distribution, and the Cauchy (Lorentz, Breit–Wigner) distribution suitable fo
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Stable Distributions and Random Walkse closely related to the simple convolution process, which is introduced first for continuous and then for discrete random variables. This leads to the central limit theorem as one of the most important results of probability theory, as well as to its generalized version which is useful in the analy
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Statistical Inference from Samplesdomly drawn from a population. The concepts of the statistic and the estimator are introduced, paying attention to their consistency and bias. Sample mean and sample variance are defined, and three most relevant sample distributions are investigated: distribution of sums and differences, distributio
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Statistical Tests: Verifying Hypothesesr probability distributions. The null and alternative hypothesis are the corner-stones of each such verification, and go hand-in-hand with the possibility of inference errors; these are defined first, followed by the exposition of standard parametric tests for normally distributed variables (tests o
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