Prediction PDF Download

Author: Daniel R. Sarewitz
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 434

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Book Description
Based upon ten case studies, Prediction explores how science-based predictions guide policy making and what this means in terms of global warming, biogenetically modifying organisms and polluting the environment with chemicals.

Author: Nicolo Cesa-Bianchi
Publisher: Cambridge University Press
ISBN: 113945482X
Category : Computers
Languages : en
Pages : 4

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Book Description
This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.

Author: Charles F. Manski
Publisher: Harvard University Press
ISBN: 9780674033665
Category : Psychology
Languages : en
Pages : 370

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Book Description
This book is a full-scale exposition of Charles Manski's new methodology for analyzing empirical questions in the social sciences. He recommends that researchers first ask what can be learned from data alone, and then ask what can be learned when data are combined with credible weak assumptions. Inferences predicated on weak assumptions, he argues, can achieve wide consensus, while ones that require strong assumptions almost inevitably are subject to sharp disagreements. Building on the foundation laid in the author's Identification Problems in the Social Sciences (Harvard, 1995), the book's fifteen chapters are organized in three parts. Part I studies prediction with missing or otherwise incomplete data. Part II concerns the analysis of treatment response, which aims to predict outcomes when alternative treatment rules are applied to a population. Part III studies prediction of choice behavior. Each chapter juxtaposes developments of methodology with empirical or numerical illustrations. The book employs a simple notation and mathematical apparatus, using only basic elements of probability theory.

Author: Andreas S. Weigend
Publisher: Routledge
ISBN: 042997227X
Category : Social Science
Languages : en
Pages : 665

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Book Description
The book is a summary of a time series forecasting competition that was held a number of years ago. It aims to provide a snapshot of the range of new techniques that are used to study time series, both as a reference for experts and as a guide for novices.

Author: Laurie Keller
Publisher: Henry Holt and Company (BYR)
ISBN: 125022599X
Category : Juvenile Fiction
Languages : en
Pages : 40

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Book Description
A potato and his eggplant nemesis struggle to find the perfect pants in this hilarious, heartwarming tale of forgiveness by bestselling Geisel-Award winning creator Laurie Keller. Potato is excited because today—for one day only— Lance Vance’s Fancy Pants Store is selling . . .POTATO PANTS! Potato rushes over early, but just as he’s about to walk in, something makes him stop. What could it be? Find out in this one-of-a-kind story about misunderstandings and forgiveness, and—of course—Potato Pants! A Christy Ottaviano Book This title has Common Core connections.

Author: Jun Zhao
Publisher: Springer
ISBN: 3319940511
Category : Computers
Languages : en
Pages : 443

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Book Description
This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.

Author: Vladimir Keilis-Borok
Publisher: Springer Science & Business Media
ISBN: 9783540435280
Category : Science
Languages : en
Pages : 358

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Book Description
The vulnerability of our civilization to earthquakes is rapidly growing, rais ing earthquakes to the ranks of major threats faced by humankind. Earth quake prediction is necessary to reduce that threat by undertaking disaster preparedness measures. This is one of the critically urgent problems whose solution requires fundamental research. At the same time, prediction is a ma jor tool of basic science, a source of heuristic constraints and the final test of theories. This volume summarizes the state-of-the-art in earthquake prediction. Its following aspects are considered: - Existing prediction algorithms and the quality of predictions they pro vide. - Application of such predictions for damage reduction, given their current accuracy, so far limited. - Fundamental understanding of the lithosphere gained in earthquake prediction research. - Emerging possibilities for major improvements of earthquake prediction methods. - Potential implications for predicting other disasters, besides earthquakes. Methodologies. At the heart of the research described here is the inte gration of three methodologies: phenomenological analysis of observations; "universal" models of complex systems such as those considered in statistical physics and nonlinear dynamics; and Earth-specific models of tectonic fault networks. In addition, the theory of optimal control is used to link earthquake prediction with earthquake preparedness.

Author: Dimitris N. Politis
Publisher: Springer
ISBN: 3319213474
Category : Mathematics
Languages : en
Pages : 246

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Book Description
The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality. Prediction has been traditionally approached via a model-based paradigm, i.e., (a) fit a model to the data at hand, and (b) use the fitted model to extrapolate/predict future data. Due to both mathematical and computational constraints, 20th century statistical practice focused mostly on parametric models. Fortunately, with the advent of widely accessible powerful computing in the late 1970s, computer-intensive methods such as the bootstrap and cross-validation freed practitioners from the limitations of parametric models, and paved the way towards the `big data' era of the 21st century. Nonetheless, there is a further step one may take, i.e., going beyond even nonparametric models; this is where the Model-Free Prediction Principle is useful. Interestingly, being able to predict a response variable Y associated with a regressor variable X taking on any possible value seems to inadvertently also achieve the main goal of modeling, i.e., trying to describe how Y depends on X. Hence, as prediction can be treated as a by-product of model-fitting, key estimation problems can be addressed as a by-product of being able to perform prediction. In other words, a practitioner can use Model-Free Prediction ideas in order to additionally obtain point estimates and confidence intervals for relevant parameters leading to an alternative, transformation-based approach to statistical inference.

Author: Edgar A. Imhoff
Publisher:
ISBN:
Category : Abandoned mined lands reclamation
Languages : en
Pages : 622

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Book Description
Contributions from city of San Francisco, Director of Emergency Services; National Science Foundation, Research Applications, Directorate; State of California, Office of Emergency Services, Seismic Safety Commission; U.S. Department of the Interior, Assistant Secretary for Energy and Minerals, Geological Survey; University of California at Los Angeles, Department of Sociology.

Author: Byung-Gyu Kim
Publisher: Springer
ISBN: 3319392417
Category : Technology & Engineering
Languages : en
Pages : 84

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Book Description
This book discusses in detail the basic algorithms of video compression that are widely used in modern video codec. The authors dissect complicated specifications and present material in a way that gets readers quickly up to speed by describing video compression algorithms succinctly, without going to the mathematical details and technical specifications. For accelerated learning, hybrid codec structure, inter- and intra- prediction techniques in MPEG-4, H.264/AVC, and HEVC are discussed together. In addition, the latest research in the fast encoder design for the HEVC and H.264/AVC is also included.