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Data Analysis
One of the strengths of this book is the author's ability to motivate the use of Bayesian methods through simple yet effective examples. - Katie St. Clair MAA Reviews.
Data Analysis
Statistics lectures have often been viewed with trepidation by engineering and science students taking an ancillary course in this subject. Whereas there are many texts showing "how" statistical methods are applied, few provide a clear explanation for non-statisticians of how the principlesof data analysis can be based on probability theory. Data Analysis: A Bayesian Tutorial provides such a text, putting emphasis as much on understanding "why" and "when" certain statistical procedures should be used as "how". This difference in approach makes the text ideal as a tutorial guide forsenior undergraduates and research students, in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. With its central emphasis on a fewfundamental rules, this book takes the mystery out of statistics by providing a clear rationale for some of the most widely-used procedures.
Data Analysis
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.
Bayesian Models of Perception and Action
An accessible introduction to constructing and interpreting Bayesian models of perceptual decision-making and action. Many forms of perception and action can be mathematically modeled as probabilistic—or Bayesian—inference, a method used to draw conclusions from uncertain evidence. According to these models, the human mind behaves like a capable data scientist or crime scene investigator when dealing with noisy and ambiguous data. This textbook provides an approachable introduction to constructing and reasoning with probabilistic models of perceptual decision-making and action. Featuring extensive examples and illustrations, Bayesian Models of Perception and Action is the first textbook to teach this widely used computational framework to beginners. Introduces Bayesian models of perception and action, which are central to cognitive science and neuroscience Beginner-friendly pedagogy includes intuitive examples, daily life illustrations, and gradual progression of complex concepts Broad appeal for students across psychology, neuroscience, cognitive science, linguistics, and mathematics Written by leaders in the field of computational approaches to mind and brain
Momentum Distributions
This volume presents the proceedings of the Workshop on Momentum Distributions held on October 24 to 26, 1988 at Argonne National Laboratory. This workshop was motivated by the enormous progress within the past few years in both experimental and theoretical studies of momentum distributions, by the growing recognition of the importance of momentum distributions to the characterization of quantum many-body systems, and especially by the realization that momentum distribution studies have much in common across the entire range of modern physics. Accordingly, the workshop was unique in that it brought together researchers in nuclear physics, electronic systems, quantum fluids and solids, and particle physics to address the common elements of momentum distribution studies. The topics dis cussed in the workshop spanned more than ten orders of magnitude range in charac teristic energy scales. The workshop included an extraordinary variety of interactions from Coulombic to hard core repulsive, from non-relativistic to extreme relativistic.
Application-driven Quantum And Statistical Physics: A Short Course For Future Scientists And Engineers - Volume 1: Foundations
'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....
Los Alamos Science
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Los Alamos Science
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New Scientist
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Mathematical Foundations of Data Science Using R
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.
