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5 results for "Bayesian statistics"
Learning Statistics With R By Daniel Navarro
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This is the PDF version of "Learning Statistics with R". The book covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing... More > on the use of the R statistical software and adopting a light, conversational style throughout. It discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com< Less
Learning Statistics with R By Daniel Navarro
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"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical... More > software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com< Less
Probabilistic Interpretation of Data By Guthrie Miller
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This book is a physicists approach to interpretation of data using Markov Chain Monte Carlo (MCMC). The concepts are derived from first principles using a style of mathematics that quickly... More > elucidates the basic ideas, sometimes with the aid of examples. Probabilistic data interpretation is a straightforward problem involving conditional probability. A prior probability distribution is essential, and examples are given. In this small book (200 pages) the reader is led from the most basic concepts of mathematical probability all the way to parallel processing algorithms for Markov Chain Monte Carlo. Fortran source code (for eigenvalue analysis of finite discrete Markov Chains, for MCMC, and for nonlinear least squares) is included with the supplementary material for this book (available online).< Less
Axioms By Robert G. Brown
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Axioms is "the last book on metaphysical philosophy you'll ever need to read". It uses the latest methods of bayesian analysis to prove that philosophy is bullshit. Instead of being the... More > end of all reason, this proves to be the proper beginning. It establishes once and for all that the most important free choice any human makes is their choice of what to believe, their axioms. Axioms are, in fact, the basis of all understanding, all knowing, but themselves cannot be known, only believed. The book connects these conclusions of western mathematical philosophy with the heart eastern philosophy in the form of Zen Buddhism, with axiomatic set theory, with Hume's skepticism, and more. It is written to be fun to read and accessible, even as it presents its core arguments fairly rigorously.< Less
Traceable S-Parameter Measurements in Coaxial Transmission Lines up to 70GHz By Johannes Hoffmann
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This thesis describes the computational process from dimensions and material parameters of coaxial standards to traceable S-parameters. Standards contain coaxial connectors and sections of... More > transmission line. Several models of the slotted coaxial connector and of rough coaxial transmission lines are discussed. The S-parameters of the standards are used to calibrate a vector network analyzer. For this purpose an overdetermined Bayesian calibration algorithm, which is described in this thesis, can be used. The calibration algorithm assumes imperfections in the definitions of the standards and in the calibration model of the vector network analyzer. Highly accurate and robust calibration is the result of the underlying statistical model. Finally, a technique to measure S-parameters in nonstandard coaxial connectors is described. It relies on the characterization of beadless adapters.< Less

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