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Focusing on Bayesian methods and maximum entropy, this book shows how a few fundamental rules can be used to tackle a variety of problems in data analysis. Topics covered include reliability analysis, multivariate optimisation, least-squares and maximum likelihood, and more.
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This volume represents the proceedings of the Ninth Annual MaxEnt Workshop, held at Dartmouth College in Hanover, New Hampshire, on August 14-18, 1989. These annual meetings are devoted to the theory and practice of Bayesian Probability and the Maximum Entropy Formalism. The fields of application exemplified at MaxEnt '89 are as diverse as the foundations of probability theory and atmospheric carbon variations, the 1987 Supernova and fundamental quantum mechanics. Subjects include sea floor drug absorption in man, pressures, neutron scattering, plasma equilibrium, nuclear magnetic resonance, radar and astrophysical image reconstruction, mass spectrometry, generalized parameter estimation, de...
'Several features make this book unusual. The first is the historical content … Second, the practical importance of quantum physics is demonstrated by the inclusion of numerous summary discussions of technological applications … A third unusual feature of this book is a detailed solution immediately following each in-text exercise. Each such problem is used to advance the discussion, and the question-and-answer format encourages the student to wrestle with the ideas personally rather than simply reading passively … This short book would easily make a helpful secondary text allowing an instructor to touch on some non-traditional topics such as least action principles and path integrals....
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The history of Oxford University Press spans five centuries of printing and publishing. This fourth volume explores the Press's modern history as an unsubsidized business with significant educational and cultural responsibilities, and how it maintained these through economic turbulence, political upheaval, and rapid technological innovation.
The aim of the book is to help students become data scientists. Since this requires a series of courses over a considerable period of time, the book intends to accompany students from the beginning to an advanced understanding of the knowledge and skills that define a modern data scientist. The book presents a comprehensive overview of the mathematical foundations of the programming language R and of its applications to data science.
Autonomous Experimentation is poised to revolutionize scientific experiments at advanced experimental facilities. Whereas previously, human experimenters were burdened with the laborious task of overseeing each measurement, recent advances in mathematics, machine learning and algorithms have alleviated this burden by enabling automated and intelligent decision-making, minimizing the need for human interference. Illustrating theoretical foundations and incorporating practitioners’ first-hand experiences, this book is a practical guide to successful Autonomous Experimentation. Despite the field’s growing potential, there exists numerous myths and misconceptions surrounding Autonomous Experimentation. Combining insights from theorists, machine-learning engineers and applied scientists, this book aims to lay the foundation for future research and widespread adoption within the scientific community. This book is particularly useful for members of the scientific community looking to improve their research methods but also contains additional insights for students and industry professionals interested in the future of the field.
This exciting and accessible book takes us on a journey from the early days of computers to the cutting-edge research of the present day that will shape computing in the coming decades. It introduces a fascinating cast of dreamers and inventors who brought these great technological developments into every corner of the modern world, and will open up the universe of computing to anyone who has ever wondered where his or her smartphone came from.