Author: Daniel R. Sarewitz Publisher: ISBN: Category : Education Languages : en Pages : 434
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: Laurie Keller Publisher: Henry Holt and Company (BYR) ISBN: 125022599X Category : Juvenile Fiction Languages : en Pages : 23
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: Vineeth Balasubramanian Publisher: Newnes ISBN: 0124017150 Category : Computers Languages : en Pages : 323
Book Description
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. - Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning - Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering - Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection
Author: Nicolo Cesa-Bianchi Publisher: Cambridge University Press ISBN: 113945482X Category : Computers Languages : en Pages : 4
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: David Shannon Publisher: Scholastic Inc. ISBN: 0545530032 Category : Juvenile Fiction Languages : en Pages : 42
Book Description
In this off-beat book perfect for reading aloud, a Caldecott Honor winner shares the story of a duck who rides a bike with hilarious results. One day down on the farm, Duck got a wild idea. “I bet I could ride a bike,” he thought. He waddled over to where the boy parked his bike, climbed on, and began to ride. At first, he rode slowly and he wobbled a lot, but it was fun! Duck rode past Cow and waved to her. “Hello, Cow!” said Duck. “Moo,” said Cow. But what she thought was, “A duck on a bike? That’s the silliest thing I’ve ever seen!” And so, Duck rides past Sheep, Horse, and all the other barnyard animals. Suddenly, a group of kids ride by on their bikes and run into the farmhouse, leaving the bikes outside. Now ALL the animals can ride bikes, just like Duck! Praise for Duck on a Bike “Shannon serves up a sunny blend of humor and action in this delightful tale of a Duck who spies a red bicycle one day and gets “a wild idea” . . . Add to all this the abundant opportunity for youngsters to chime in with barnyard responses (“M-o-o-o”; “Cluck! Cluck!”), and the result is one swell read-aloud, packed with freewheeling fun.” —Publishers Weekly “Grab your funny bone—Shannon . . . rides again! . . . A “quackerjack” of a terrific escapade.” —Kirkus Reviews
Author: Dimitris N. Politis Publisher: Springer ISBN: 3319213474 Category : Mathematics Languages : en Pages : 256
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: Jun Zhao Publisher: Springer ISBN: 3319940511 Category : Computers Languages : en Pages : 453
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: Lev M. Klyatis Publisher: John Wiley & Sons ISBN: 1119411939 Category : Technology & Engineering Languages : en Pages : 289
Book Description
This textbook reviews the methodologies of reliability prediction as currently used in industries such as electronics, automotive, aircraft, aerospace, off-highway, farm machinery, and others. It then discusses why these are not successful; and, presents methods developed by the authors for obtaining accurate information for successful prediction. The approach is founded on approaches that accurately duplicate the real world use of the product. Their approach is based on two fundamental components needed for successful reliability prediction; first, the methodology necessary; and, second, use of accelerated reliability and durability testing as a source of the necessary data. Applicable to all areas of engineering, this textbook details the newest techniques and tools to achieve successful reliabilityprediction and testing. It demonstrates practical examples of the implementation of the approaches described. This book is a tool for engineers, managers, researchers, in industry, teachers, and students. The reader will learn the importance of the interactions of the influencing factors and the interconnections of safety and human factors in product prediction and testing.
Author: Byung-Gyu Kim Publisher: Springer ISBN: 3319392417 Category : Technology & Engineering Languages : en Pages : 90
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.
Author: Igor Tulchinsky Publisher: MIT Press ISBN: 026237319X Category : Business & Economics Languages : en Pages : 232
Book Description
The power of the ever-increasing tools and algorithms for prediction and their paradoxical effects on risk. The Age of Prediction is about two powerful, and symbiotic, trends: the rapid development and use of artificial intelligence and big data to enhance prediction, as well as the often paradoxical effects of these better predictions on our understanding of risk and the ways we live. Beginning with dramatic advances in quantitative investing and precision medicine, this book explores how predictive technology is quietly reshaping our world in fundamental ways, from crime fighting and warfare to monitoring individual health and elections. As prediction grows more robust, it also alters the nature of the accompanying risk, setting up unintended and unexpected consequences. The Age of Prediction details how predictive certainties can bring about complacency or even an increase in risks—genomic analysis might lead to unhealthier lifestyles or a GPS might encourage less attentive driving. With greater predictability also comes a degree of mystery, and the authors ask how narrower risks might affect markets, insurance, or risk tolerance generally. Can we ever reduce risk to zero? Should we even try? This book lays an intriguing groundwork for answering these fundamental questions and maps out the latest tools and technologies that power these projections into the future, sometimes using novel, cross-disciplinary tools to map out cancer growth, people’s medical risks, and stock dynamics.
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Author: Daniel R. Sarewitz
Author: Laurie Keller
Author: Vineeth Balasubramanian
Author: Nicolo Cesa-Bianchi
Author: David Shannon
Author: Dimitris N. Politis
Author: Jun Zhao
Author: Lev M. Klyatis
Author: Byung-Gyu Kim
Author: Igor Tulchinsky