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"Why should we use white noise analysis? Well, one reason of course is that it fills that earlier gap in the tool kit. As Hida would put it, white noise provides us with a useful set of independent coordinates, parametrized by "time". And there is a feature which makes white noise analysis extremely user-friendly. Typically the physicist — and not only he — sits there with some heuristic ansatz, like e.g. the famous Feynman "integral", wondering whether and how this might make sense mathematically. In many cases the characterization theorem of white noise analysis provides the user with a sweet and easy answer. Feynman's "integral" can now be understood, the "It's all in the vacuum" ansatz of Haag and Coester is now making sense via Dirichlet forms, and so on in many fields of application. There is mathematical finance, there have been applications in biology, and engineering, many more than we could collect in the present volume. Finally, there is one extra benefit: when we internalize the structures of Gaussian white noise analysis we will be ready to meet another close relative. We will enjoy the important similarities and differences which we encounter in the Poisson case, championed in particular by Y Kondratiev and his group. Let us look forward to a companion volume on the uses of Poisson white noise. The present volume is more than a collection of autonomous contributions. The introductory chapter on white noise analysis was made available to the other authors early on for reference and to facilitate conceptual and notational coherence in their work."--Publisher's website.
Why should we use white noise analysis? Well, one reason of course is that it fills that earlier gap in the tool kit. As Hida would put it, white noise provides us with a useful set of independent coordinates, parametrized by 'time'. And there is a feature which makes white noise analysis extremely user-friendly. Typically the physicist — and not only he — sits there with some heuristic ansatz, like e.g. the famous Feynman 'integral', wondering whether and how this might make sense mathematically. In many cases the characterization theorem of white noise analysis provides the user with a sweet and easy answer. Feynman's 'integral' can now be understood, the 'It's all in the vacuum' ansatz of Haag and Coester is now making sense via Dirichlet forms, and so on in many fields of application. There is mathematical finance, there have been applications in biology, and engineering, many more than we could collect in the present volume.Finally, there is one extra benefit: when we internalize the structures of Gaussian white noise analysis we will be ready to meet another close relative. We will enjoy the important similarities and differences which we encounter in the Poisson case, championed in particular by Y Kondratiev and his group. Let us look forward to a companion volume on the uses of Poisson white noise.The present volume is more than a collection of autonomous contributions. The introductory chapter on white noise analysis was made available to the other authors early on for reference and to facilitate conceptual and notational coherence in their work.
NATIONAL BOOK AWARD WINNER • An “eerie, brilliant, and touching” (The New York Times) modern classic about mass culture and the numbing effects of technology. “Tremendously funny . . . A stunning performance from one of our most intelligent novelists.”—The New Republic The inspiration for the award-winning major motion picture starring Adam Driver and Greta Gerwig Jack Gladney teaches Hitler Studies at a liberal arts college in Middle America where his colleagues include New York expatriates who want to immerse themselves in “American magic and dread.” Jack and his fourth wife, Babette, bound by their love, fear of death, and four ultramodern offspring, navigate the usual rocky passages of family life to the background babble of brand-name consumerism. Then a lethal black chemical cloud floats over their lives, an “airborne toxic event” unleashed by an industrial accident. The menacing cloud is a more urgent and visible version of the “white noise” engulfing the Gladney family—radio transmissions, sirens, microwaves, ultrasonic appliances, and TV murmurings—pulsing with life, yet suggesting something ominous.
This proceedings contains articles on white noise analysis and related subjects. Applications in various branches of science are also discussed. White noise analysis stems from considering the time derivative of Brownian motion (“white noise”) as the basic ingredient of an infinite dimensional calculus. It provides a powerful mathematical tool for research fields such as stochastic analysis, potential theory in infinite dimensions and quantum field theory.
The National Book Award-winning classic from the author of Underworld and Libra, now a major motion picture starring Adam Driver and Greta Gerwig White Noise tells the story of Jack Gladney, his fourth wife, Babette, and four ultra­modern offspring as they navigate the rocky passages of family life to the background babble of brand-name consumerism. When an industrial accident unleashes an "airborne toxic event," a lethal black chemical cloud floats over their lives. The menacing cloud is a more urgent and visible version of the "white noise" engulfing the Gladneys—radio transmissions, sirens, microwaves, ultrasonic appliances, and TV murmurings—pulsing with life, yet suggesting something ominous. For more than sixty-five years, Penguin has been the leading publisher of classic literature in the English-speaking world. With more than 1,500 titles, Penguin Classics represents a global bookshelf of the best works throughout history and across genres and disciplines. Readers trust the series to provide authoritative texts enhanced by introductions and notes by distinguished scholars and contemporary authors, as well as up-to-date translations by award-winning translators.
The topics discussed in this book can be classified into three parts: . (i) Gaussian processes. The most general and in fact final representation theory of Gaussian processes is included in this book. This theory is still referred to often and its developments are discussed. (ii) White noise analysis. This book includes the notes of the series of lectures delivered in 1975 at Carleton University in Ottawa. They describe the very original idea of introducing the notion of generalized Brownian functionals (nowadays called OC generalized white noise functionalsOCO, and sometimes OC Hida distributionOCO. (iii) Variational calculus for random fields. This topic will certainly represent one of the driving research lines for probability theory in the next century, as can be seen from several papers in this volume. Sample Chapter(s). Chapter 1: Analysis of Brownian Functionals (1,502 KB). Contents: General Theory of White Noise Functionals; Gaussian and Other Processes; Infinite Dimensional Harmonic Analysis and Rotation Group; Quantum Theory; Feynman Integrals and Random Fields; Variational Calculus and Random Fields; Application to Biology. Readership: Graduate students and researchers in the fields of probability theory, functional analysis, statistics and theoretical physics."
This volume includes papers by leading mathematicians in the fields of stochastic analysis, white noise theory and quantum information, together with their applications. The papers selected were presented at the International Conference on Stochastic Analysis: Classical and Quantum held at Meijo University, Nagoya, Japan from 1 to 5 November 2004. The large range of subjects covers the latest research in probability theory.
Understanding Jonathan Lethem is a study of the novels, short fiction, and nonfiction on a wide range of subjects in the arts by American novelist Jonathan Lethem, who is the recipient of the National Book Critics Circle Award for Fiction for Motherless Brooklyn, a MacArthur Foundation "genius" grant, and the Locus Award for Best First Novel for Gun, with Occasional Music. Matthew Luter explores the key contemporaries of and influences on Lethem, who is the Roy Edward Disney Professor of Creative Writing at Pomona College. Luter begins this volume by explaining how Lethem's innovative and provocative essay on creative appropriation, "The Ecstasy of Influence," differs from other writing about influence, suggesting an artistic mode that celebrates thoughtful borrowing. Readings of Lethem's three major novels follow: taken together, Motherless Brooklyn, The Fortress of Solitude, and Chronic City present a novelist coming to terms with the joys and downsides of artistic influence. Luter concludes the edition with an examination of Lethem's third collection, Lucky Alan: And Other Stories. Borrowing openly and promiscuously from earlier traditions both high and low (experimental fiction, comic books, art film, detective novels), Lethem displays a career-long interest in questioning what literary originality might mean in a postmodern age. Some suggest that such borrowings indicate a literary well that has run dry, making writers such as Lethem mere patchwork artists. Luter argues instead that Lethem's propensity for wearing his influences and obsessions on his sleeve encourages new thought about originality itself. Out with "it's all been done" and in with "look at all that's been done, and all that we can still do with it!"
This Springer brief provides the necessary foundations to understand differential privacy and describes practical algorithms enforcing this concept for the publication of real-time statistics based on sensitive data. Several scenarios of interest are considered, depending on the kind of estimator to be implemented and the potential availability of prior public information about the data, which can be used greatly to improve the estimators' performance. The brief encourages the proper use of large datasets based on private data obtained from individuals in the world of the Internet of Things and participatory sensing. For the benefit of the reader, several examples are discussed to illustrate the concepts and evaluate the performance of the algorithms described. These examples relate to traffic estimation, sensing in smart buildings, and syndromic surveillance to detect epidemic outbreaks.
Inventive Engineering is an emerging engineering science focused on the conceptual designing processes whereby creative, or inventive, designs are developed. Its core concepts are too often unknown and even surprising, but they are also feasible and can be learned, leading to potentially patentable designs. Inventive engineers have a tremendou