Data Analysis Techniques for Physical Scientists

This book PDF is perfect for those who love Science genre, written by Claude Pruneau and published by Cambridge University Press which was released on 30 September 2017 with total hardcover pages null. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Data Analysis Techniques for Physical Scientists books below.

Data Analysis Techniques for Physical Scientists
Author : Claude Pruneau
File Size : 43,6 Mb
Publisher : Cambridge University Press
Language : English
Release Date : 30 September 2017
ISBN : 9781108416788
Pages : null pages
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Data Analysis Techniques for Physical Scientists by Claude Pruneau Book PDF Summary

A comprehensive guide to data analysis techniques for the physical sciences including probability, statistics, data reconstruction, data correction and Monte Carlo methods. This book provides a valuable resource for advanced undergraduate and graduate students, as well as practitioners in the fields of experimental particle physics, nuclear physics and astrophysics.

Data Analysis Techniques for Physical Scientists

A comprehensive guide to data analysis techniques for the physical sciences including probability, statistics, data reconstruction, data correction and Monte Carlo methods. This book provides a valuable resource for advanced undergraduate and graduate students, as well as practitioners in the fields of experimental particle physics, nuclear physics and astrophysics.

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Now thoroughly revised and up-dated, this book describes techniques for handling and analysing data obtained from high-energy and nuclear physics experiments. The observation of particle interactions involves the analysis of large and complex data samples. Beginning with a chapter on real-time data triggering and filtering, the book describes methods of

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Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides

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