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Titlebook: Probability and Statistics in Experimental Physics; Byron P. Roe Textbook 2001Latest edition Springer-Verlag New York 2001 Binomial distri

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書目名稱Probability and Statistics in Experimental Physics
編輯Byron P. Roe
視頻videohttp://file.papertrans.cn/757/756941/756941.mp4
叢書名稱Undergraduate Texts in Contemporary Physics
圖書封面Titlebook: Probability and Statistics in Experimental Physics;  Byron P. Roe Textbook 2001Latest edition Springer-Verlag New York 2001 Binomial distri
描述Intended for advanced undergraduates and graduate students, this book is a practical guide to the use of probability and statistics in experimental physics. The emphasis is on applications and understanding, on theorems and techniques actually used in research. The text is not a comprehensive text in probability and statistics; proofs are sometimes omitted if they do not contribute to intuition in understanding the theorem. The problems, some with worked solutions, introduce the student to the use of computers; occasional reference is made to routines available in the CERN library, but other systems, such as Maple, can also be used. Topics covered include: basic concepts; definitions; some simple results independent of specific distributions; discrete distributions; the normal and other continuous distributions; generating and characteristic functions; the Monte Carlo method and computer simulations; multi-dimensional distributions; the central limit theorem; inverse probability and confidence belts; estimation methods; curve fitting and likelihood ratios; interpolating functions; fitting data with constraints; robust estimation methods. This second edition introduces a new method
出版日期Textbook 2001Latest edition
關(guān)鍵詞Binomial distribution; Curve fitting; Experiment; Experimental Physics; Fitting; Maple; Measure; Monte Carl
版次2
doihttps://doi.org/10.1007/978-1-4684-9296-5
isbn_softcover978-1-4419-2895-5
isbn_ebook978-1-4684-9296-5
copyrightSpringer-Verlag New York 2001
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Byron P. Roemerous practical industrial applications, e.g. AI, robotics,Computational complexity theory provides a framework for understanding the cost of solving computational problems, as measured by the requirement for resources such as time and space. The objects of study are algorithms defined within a for
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Byron P. Roemerous practical industrial applications, e.g. AI, robotics,Computational complexity theory provides a framework for understanding the cost of solving computational problems, as measured by the requirement for resources such as time and space. The objects of study are algorithms defined within a for
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Byron P. Roeversity jointly sponsored a conference on "The Complexity and Self-organization in Socio- economic Systems" on October 17-20, 1994 at Beijing, China. The purpose of the conference was to explore the complexity and evolutionary laws of socio- economic systems through nonlinear dynamic systems and sel
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