Principles Of Uncertainty
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The Principles of Uncertainty
“Sublime . . . Kalman’s elegantly witty and at times melancholy narrative runs arm in arm with her unmistakable paintings on a serendipitous romp through the history of the world.” —Vanity Fair “Wildly original . . . there’s nothing else even remotely like it . . . This hilarious, wise, and deeply moving volume [is] the ultimate picture book for grown-ups.” —O Magazine Maira Kalman paints her highly personal worldview in this inimitable combination of image and text An irresistible invitation to experience life through a beloved artist's psyche, The Principles of Uncertainty is a compilation of Maira Kalman's New York Times columns. Part personal narrative, part documentary, part travelogue, part chapbook, and all Kalman, these brilliant, whimsical paintings, ideas, and images - which initially appear random - ultimately form an intricately interconnected worldview, an idiosyncratic inner monologue.
Principles of Uncertainty
Praise for the first edition: Principles of Uncertainty is a profound and mesmerising book on the foundations and principles of subjectivist or behaviouristic Bayesian analysis. ... the book is a pleasure to read. And highly recommended for teaching as it can be used at many different levels. ... A must-read for sure!—Christian Robert, CHANCE It's a lovely book, one that I hope will be widely adopted as a course textbook. —Michael Jordan, University of California, Berkeley, USA Like the prize-winning first edition, Principles of Uncertainty, Second Edition is an accessible, comprehensive text on the theory of Bayesian Statistics written in an appealing, inviting style, and packed with interesting examples. It presents an introduction to the subjective Bayesian approach which has played a pivotal role in game theory, economics, and the recent boom in Markov Chain Monte Carlo methods. This new edition has been updated throughout and features new material on Nonparametric Bayesian Methods, the Dirichlet distribution, a simple proof of the central limit theorem, and new problems. Key Features: First edition won the 2011 DeGroot Prize Well-written introduction to theory of Bayesian statistics Each of the introductory chapters begins by introducing one new concept or assumption Uses "just-in-time mathematics"—the introduction to mathematical ideas just before they are applied
The Uncertainty Principle in Harmonic Analysis
Author: Victor Havin
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
The present book is a collection of variations on a theme which can be summed up as follows: It is impossible for a non-zero function and its Fourier transform to be simultaneously very small. In other words, the approximate equalities x :::::: y and x :::::: fj cannot hold, at the same time and with a high degree of accuracy, unless the functions x and yare identical. Any information gained about x (in the form of a good approximation y) has to be paid for by a corresponding loss of control on x, and vice versa. Such is, roughly speaking, the import of the Uncertainty Principle (or UP for short) referred to in the title ofthis book. That principle has an unmistakable kinship with its namesake in physics - Heisenberg's famous Uncertainty Principle - and may indeed be regarded as providing one of mathematical interpretations for the latter. But we mention these links with Quantum Mechanics and other connections with physics and engineering only for their inspirational value, and hasten to reassure the reader that at no point in this book will he be led beyond the world of purely mathematical facts. Actually, the portion of this world charted in our book is sufficiently vast, even though we confine ourselves to trigonometric Fourier series and integrals (so that "The U. P. in Fourier Analysis" might be a slightly more appropriate title than the one we chose).