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| definition of prediction in science: Reproducibility and Replicability in Science National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Committee on Science, Engineering, Medicine, and Public Policy, Board on Research Data and Information, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Division on Earth and Life Studies, Nuclear and Radiation Studies Board, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Board on Behavioral, Cognitive, and Sensory Sciences, Committee on Reproducibility and Replicability in Science, 2019-10-20 One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science. |
| definition of prediction in science: Prediction Daniel R. Sarewitz, Roger A. Pielke (Jr.), Radford Byerly, Roger A. Pielke, 2000-04 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. |
| definition of prediction in science: Fundamentals of Clinical Data Science Pieter Kubben, Michel Dumontier, Andre Dekker, 2018-12-21 This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience. |
| definition of prediction in science: Understanding Risk in Criminal Justice Hazel Kemshall, 2003-08-16 “the Crime & Justice series has become a key resource for universities in teaching criminology and criminal justice… Professor Kemshall has established herself as a leading figure in the concepts of risk, risk management and public protection issues… an invaluable read for those entering Criminal Justice or moving to work in projects or teams at the forefront of public protection” Vista How significant is risk to the formation and implementation of penal policy? To what extent are the tasks and activities of frontline criminal justice workers informed by concerns to assess and manage risk? Has there been a significant 'sea-change' in the delivery of criminal justice, and if so, what are the future implications of this? This illuminating text examines the significance of the concept of risk in criminal justice policy, and in the role of criminal justice agencies and crime prevention initiatives. Particular features of the book include its use of practical examples, coverage of previously unpublished research, and a full review of current risk assessment tools for use with offenders. It is designed with undergraduate courses in mind, providing frequent summaries, lists of further reading, and a glossary. The identification, assessment and management of risk has become a central theme of criminal justice policy. For some penal policy commentators this represents a 'sea-change' in crime management to a new era of 'actuarial justice', that is the management of crime opportunities and risk distribution rather than the management of individual offenders. By drawing on key areas of criminal justice practice such as policing, probation and crime prevention, this book examines the actual extent of this change and reviews the case for a new risk-based penology. The book combines a review of current theories on actuarial justice with a detailed examination of current practices in key frontline agencies. The result is an essential text for criminology students and trainee professionals in criminal justice. |
| definition of prediction in science: A Framework for K-12 Science Education National Research Council, Division of Behavioral and Social Sciences and Education, Board on Science Education, Committee on a Conceptual Framework for New K-12 Science Education Standards, 2012-02-28 Science, engineering, and technology permeate nearly every facet of modern life and hold the key to solving many of humanity's most pressing current and future challenges. The United States' position in the global economy is declining, in part because U.S. workers lack fundamental knowledge in these fields. To address the critical issues of U.S. competitiveness and to better prepare the workforce, A Framework for K-12 Science Education proposes a new approach to K-12 science education that will capture students' interest and provide them with the necessary foundational knowledge in the field. A Framework for K-12 Science Education outlines a broad set of expectations for students in science and engineering in grades K-12. These expectations will inform the development of new standards for K-12 science education and, subsequently, revisions to curriculum, instruction, assessment, and professional development for educators. This book identifies three dimensions that convey the core ideas and practices around which science and engineering education in these grades should be built. These three dimensions are: crosscutting concepts that unify the study of science through their common application across science and engineering; scientific and engineering practices; and disciplinary core ideas in the physical sciences, life sciences, and earth and space sciences and for engineering, technology, and the applications of science. The overarching goal is for all high school graduates to have sufficient knowledge of science and engineering to engage in public discussions on science-related issues, be careful consumers of scientific and technical information, and enter the careers of their choice. A Framework for K-12 Science Education is the first step in a process that can inform state-level decisions and achieve a research-grounded basis for improving science instruction and learning across the country. The book will guide standards developers, teachers, curriculum designers, assessment developers, state and district science administrators, and educators who teach science in informal environments. |
| definition of prediction in science: Practical Statistics for Data Scientists Peter Bruce, Andrew Bruce, 2017-05-10 Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data |
| definition of prediction in science: Predictive Analytics Eric Siegel, 2016-01-12 Mesmerizing & fascinating... —The Seattle Post-Intelligencer The Freakonomics of big data. —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a |
| definition of prediction in science: Prediction, Learning, and Games Nicolo Cesa-Bianchi, Gabor Lugosi, 2006-03-13 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. |
| definition of prediction in science: Memory as Prediction Tomaso Vecchi, Daniele Gatti, 2020-11-24 Theoretical reflections and analytical observations on memory and prediction, linking these concepts to the role of the cerebellum in higher cognition. What is memory? What is memory for? Where is memory in the brain? Although memory is probably the most studied function in cognition, these fundamental questions remain challenging. We can try to answer the question of memory's purpose by defining the function of memory as remembering the past. And yet this definition is not consistent with the many errors that characterize our memory, or with the phylogenetic and ontogenetic origin of memory. In this book, Tomaso Vecchi and Daniele Gatti argue that the purpose of memory is not to remember the past but to predict the future. |
| definition of prediction in science: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| definition of prediction in science: Prediction, Projection and Forecasting Thomas L. Saaty, Luis G. Vargas, 2012-12-16 We predict when we say in advance, foretell, or prophesy what is likely to happen in the future. We project when we calculate the numerical value associated with a future event. We forecast, a special kind of prediction, on data of past happenings to generate or cast data for future by relying happenings. Generally, one predicts (yes, no) a war, an earthquake or the outcome of a chess match, projects the value of the GNP or of unemployment, and forecasts the weather and, more scientifically, the economic trends. Prediction, projection, and forecasting must be constrained in time and space: when and where. Often the accuracy of a forecast is of interest along with how sensitive the outcome is to changes in the factors involved. Is there a basis for improving the wisdom we need to make correct and useful predictions? We believe there is, and that it can be cultivated by studying the approach given here along with the various examples. To the best of our knowledge, no other work has approached prediction in the scientific framework of hierarchies. Prediction is the synthesis of past and present in an attempt to foretell the future. In our view, creation is not the ultimate phenomenon of the world. Nature creates forms and so do we. The problem is to surmise the eventual purpose, impact, and use of creation. It is the synthesis or outcome of bringing together the results of creation that we need to predict. |
| definition of prediction in science: Interactions I. Bernard Cohen, 1994 One of the fruits of the scientific revolution was the idea of a social science that would operate in ways comparable to the newly triumphant natural sciences. This text offers a historical perspective on the interactions between the social and natural sciences. |
| definition of prediction in science: The Philosophy of Science Sahotra Sarkar, Jessica Pfeifer, 2006 The first in-depth reference to the field that combines scientific knowledge with philosophical inquiry, this encyclopedia brings together a team of leading scholars to provide nearly 150 entries on the essential concepts in the philosophy of science. The areas covered include biology, chemistry, epistemology and metaphysics, physics, psychology and mind, the social sciences, and key figures in the combined studies of science and philosophy. (Midwest). |
| definition of prediction in science: Expert Political Judgment Philip E. Tetlock, 2017-08-29 Since its original publication, Expert Political Judgment by New York Times bestselling author Philip Tetlock has established itself as a contemporary classic in the literature on evaluating expert opinion. Tetlock first discusses arguments about whether the world is too complex for people to find the tools to understand political phenomena, let alone predict the future. He evaluates predictions from experts in different fields, comparing them to predictions by well-informed laity or those based on simple extrapolation from current trends. He goes on to analyze which styles of thinking are more successful in forecasting. Classifying thinking styles using Isaiah Berlin's prototypes of the fox and the hedgehog, Tetlock contends that the fox--the thinker who knows many little things, draws from an eclectic array of traditions, and is better able to improvise in response to changing events--is more successful in predicting the future than the hedgehog, who knows one big thing, toils devotedly within one tradition, and imposes formulaic solutions on ill-defined problems. He notes a perversely inverse relationship between the best scientific indicators of good judgement and the qualities that the media most prizes in pundits--the single-minded determination required to prevail in ideological combat. Clearly written and impeccably researched, the book fills a huge void in the literature on evaluating expert opinion. It will appeal across many academic disciplines as well as to corporations seeking to develop standards for judging expert decision-making. Now with a new preface in which Tetlock discusses the latest research in the field, the book explores what constitutes good judgment in predicting future events and looks at why experts are often wrong in their forecasts. |
| definition of prediction in science: Science And Human Behavior B.F Skinner, 2012-12-18 The psychology classic—a detailed study of scientific theories of human nature and the possible ways in which human behavior can be predicted and controlled—from one of the most influential behaviorists of the twentieth century and the author of Walden Two. “This is an important book, exceptionally well written, and logically consistent with the basic premise of the unitary nature of science. Many students of society and culture would take violent issue with most of the things that Skinner has to say, but even those who disagree most will find this a stimulating book.” —Samuel M. Strong, The American Journal of Sociology “This is a remarkable book—remarkable in that it presents a strong, consistent, and all but exhaustive case for a natural science of human behavior…It ought to be…valuable for those whose preferences lie with, as well as those whose preferences stand against, a behavioristic approach to human activity.” —Harry Prosch, Ethics |
| definition of prediction in science: Duck on a Bike David Shannon, 2016-07-26 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 |
| definition of prediction in science: Interpretable Machine Learning Christoph Molnar, 2020 This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project. |
| definition of prediction in science: Scientific Research in Education National Research Council, Division of Behavioral and Social Sciences and Education, Center for Education, Committee on Scientific Principles for Education Research, 2002-03-28 Researchers, historians, and philosophers of science have debated the nature of scientific research in education for more than 100 years. Recent enthusiasm for evidence-based policy and practice in educationâ€now codified in the federal law that authorizes the bulk of elementary and secondary education programsâ€have brought a new sense of urgency to understanding the ways in which the basic tenets of science manifest in the study of teaching, learning, and schooling. Scientific Research in Education describes the similarities and differences between scientific inquiry in education and scientific inquiry in other fields and disciplines and provides a number of examples to illustrate these ideas. Its main argument is that all scientific endeavors share a common set of principles, and that each fieldâ€including education researchâ€develops a specialization that accounts for the particulars of what is being studied. The book also provides suggestions for how the federal government can best support high-quality scientific research in education. |
| definition of prediction in science: Grit Angela Duckworth, 2016-05-03 In this instant New York Times bestseller, Angela Duckworth shows anyone striving to succeed that the secret to outstanding achievement is not talent, but a special blend of passion and persistence she calls “grit.” “Inspiration for non-geniuses everywhere” (People). The daughter of a scientist who frequently noted her lack of “genius,” Angela Duckworth is now a celebrated researcher and professor. It was her early eye-opening stints in teaching, business consulting, and neuroscience that led to her hypothesis about what really drives success: not genius, but a unique combination of passion and long-term perseverance. In Grit, she takes us into the field to visit cadets struggling through their first days at West Point, teachers working in some of the toughest schools, and young finalists in the National Spelling Bee. She also mines fascinating insights from history and shows what can be gleaned from modern experiments in peak performance. Finally, she shares what she’s learned from interviewing dozens of high achievers—from JP Morgan CEO Jamie Dimon to New Yorker cartoon editor Bob Mankoff to Seattle Seahawks Coach Pete Carroll. “Duckworth’s ideas about the cultivation of tenacity have clearly changed some lives for the better” (The New York Times Book Review). Among Grit’s most valuable insights: any effort you make ultimately counts twice toward your goal; grit can be learned, regardless of IQ or circumstances; when it comes to child-rearing, neither a warm embrace nor high standards will work by themselves; how to trigger lifelong interest; the magic of the Hard Thing Rule; and so much more. Winningly personal, insightful, and even life-changing, Grit is a book about what goes through your head when you fall down, and how that—not talent or luck—makes all the difference. This is “a fascinating tour of the psychological research on success” (The Wall Street Journal). |
| definition of prediction in science: How to Build Social Science Theories Pamela J. Shoemaker, James William Tankard, Jr., Dominic L. Lasorsa, 2003-12-10 Click ′Additional Materials′ to read the foreword by Jerald Hage As straightforward as its title, How to Build Social Science Theories sidesteps the well-traveled road of theoretical examination by demonstrating how new theories originate and how they are elaborated. Essential reading for students of social science research, this book traces theories from their most rudimentary building blocks (terminology and definitions) through multivariable theoretical statements, models, the role of creativity in theory building, and how theories are used and evaluated. Authors Pamela J. Shoemaker, James William Tankard, Jr., and Dominic L. Lasorsa intend to improve research in many areas of the social sciences by making research more theory-based and theory-oriented. The book begins with a discussion of concepts and their theoretical and operational definitions. It then proceeds to theoretical statements, including hypotheses, assumptions, and propositions. Theoretical statements need theoretical linkages and operational linkages; this discussion begins with bivariate relationships, as well as three-variable, four-variable, and further multivariate relationships. The authors also devote chapters to the creative component of theory-building and how to evaluate theories. How to Build Social Science Theories is a sophisticated yet readable analysis presented by internationally known experts in social science methodology. It is designed primarily as a core text for graduate and advanced undergraduate courses in communication theory. It will also be a perfect addition to any course dealing with theory and research methodology across the social sciences. Additionally, professional researchers will find it an indispensable guide to the genesis, dissemination, and evaluation of social science theories. |
| definition of prediction in science: Stumbling on Happiness Daniel Gilbert, 2009-02-24 A smart and funny book by a prominent Harvard psychologist, which uses groundbreaking research and (often hilarious) anecdotes to show us why we’re so lousy at predicting what will make us happy – and what we can do about it. Most of us spend our lives steering ourselves toward the best of all possible futures, only to find that tomorrow rarely turns out as we had expected. Why? As Harvard psychologist Daniel Gilbert explains, when people try to imagine what the future will hold, they make some basic and consistent mistakes. Just as memory plays tricks on us when we try to look backward in time, so does imagination play tricks when we try to look forward. Using cutting-edge research, much of it original, Gilbert shakes, cajoles, persuades, tricks and jokes us into accepting the fact that happiness is not really what or where we thought it was. Among the unexpected questions he poses: Why are conjoined twins no less happy than the general population? When you go out to eat, is it better to order your favourite dish every time, or to try something new? If Ingrid Bergman hadn’t gotten on the plane at the end of Casablanca, would she and Bogey have been better off? Smart, witty, accessible and laugh-out-loud funny, Stumbling on Happiness brilliantly describes all that science has to tell us about the uniquely human ability to envision the future, and how likely we are to enjoy it when we get there. |
| definition of prediction in science: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| definition of prediction in science: Sub-seasonal to Seasonal Prediction Andrew Robertson, Frederic Vitart, 2018-10-19 The Gap Between Weather and Climate Forecasting: Sub-seasonal to Seasonal Prediction is an ideal reference for researchers and practitioners across the range of disciplines involved in the science, modeling, forecasting and application of this new frontier in sub-seasonal to seasonal (S2S) prediction. It provides an accessible, yet rigorous, introduction to the scientific principles and sources of predictability through the unique challenges of numerical simulation and forecasting with state-of-science modeling codes and supercomputers. Additional coverage includes the prospects for developing applications to trigger early action decisions to lessen weather catastrophes, minimize costly damage, and optimize operator decisions. The book consists of a set of contributed chapters solicited from experts and leaders in the fields of S2S predictability science, numerical modeling, operational forecasting, and developing application sectors. The introduction and conclusion, written by the co-editors, provides historical perspective, unique synthesis and prospects, and emerging opportunities in this exciting, complex and interdisciplinary field. - Contains contributed chapters from leaders and experts in sub-seasonal to seasonal science, forecasting and applications - Provides a one-stop shop for graduate students, academic and applied researchers, and practitioners in an emerging and interdisciplinary field - Offers a synthesis of the state of S2S science through the use of concrete examples, enabling potential users of S2S forecasts to quickly grasp the potential for application in their own decision-making - Includes a broad set of topics, illustrated with graphic examples, that highlight interdisciplinary linkages |
| definition of prediction in science: Drug-like Properties: Concepts, Structure Design and Methods Li Di, Edward H Kerns, 2010-07-26 Of the thousands of novel compounds that a drug discovery project team invents and that bind to the therapeutic target, typically only a fraction of these have sufficient ADME/Tox properties to become a drug product. Understanding ADME/Tox is critical for all drug researchers, owing to its increasing importance in advancing high quality candidates to clinical studies and the processes of drug discovery. If the properties are weak, the candidate will have a high risk of failure or be less desirable as a drug product. This book is a tool and resource for scientists engaged in, or preparing for, the selection and optimization process. The authors describe how properties affect in vivo pharmacological activity and impact in vitro assays. Individual drug-like properties are discussed from a practical point of view, such as solubility, permeability and metabolic stability, with regard to fundamental understanding, applications of property data in drug discovery and examples of structural modifications that have achieved improved property performance. The authors also review various methods for the screening (high throughput), diagnosis (medium throughput) and in-depth (low throughput) analysis of drug properties. - Serves as an essential working handbook aimed at scientists and students in medicinal chemistry - Provides practical, step-by-step guidance on property fundamentals, effects, structure-property relationships, and structure modification strategies - Discusses improvements in pharmacokinetics from a practical chemist's standpoint |
| definition of prediction in science: Concepts of Biology Samantha Fowler, Rebecca Roush, James Wise, 2023-05-12 Black & white print. Concepts of Biology is designed for the typical introductory biology course for nonmajors, covering standard scope and sequence requirements. The text includes interesting applications and conveys the major themes of biology, with content that is meaningful and easy to understand. The book is designed to demonstrate biology concepts and to promote scientific literacy. |
| definition of prediction in science: Social Science Research Anol Bhattacherjee, 2012-04-01 This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages. |
| definition of prediction in science: Scientific Explanation Philip Kitcher, Wesley C. Salmon, 1962-05-25 Scientific Explanation was first published in 1962. Minnesota Archive Editions uses digital technology to make long-unavailable books once again accessible, and are published unaltered from the original University of Minnesota Press editions. Is a new consensus emerging in the philosophy of science? The nine distinguished contributors to this volume apply that question to the realm of scientific explanation and, although their conclusions vary, they agree in one respect: there definitely was an old consensus. Co-editor Wesley Salmon's opening essay, Four Decades of Scientific Explanation, grounds the entire discussion. His point of departure is the founding document of the old consensus: a 1948 paper by Carl G. Hempel and Paul Oppenheim, Studies in the Logic of Explanation, that set forth, with remarkable clarity, a mode of argument that came to be known as the deductive-nomological model. This approach, holding that explanation dies not move beyond the sphere of empirical knowledge, remained dominant during the hegemony of logical empiricism from 1950 to 1975. Salmon traces in detail the rise and breakup of the old consensus, and examines the degree to which there is, if not a new consensus, at least a kind of reconciliation on this issue among contemporary philosophers of science and clear agreement that science can indeed tell us why. The other contributors, in the order of their presentations, are: Peter Railton, Matti Sintonen, Paul W. Humphreys, David Papineau, Nancy Cartwright, James Woodward, Merrilee H. Salmon, and Philip Kitcher. |
| definition of prediction in science: Scientific Discovery in the Social Sciences Mark Addis, Peter C. R. Lane, Peter D. Sozou, Fernand Gobet, 2019-09-12 This volume offers selected papers exploring issues arising from scientific discovery in the social sciences. It features a range of disciplines including behavioural sciences, computer science, finance, and statistics with an emphasis on philosophy. The first of the three parts examines methods of social scientific discovery. Chapters investigate the nature of causal analysis, philosophical issues around scale development in behavioural science research, imagination in social scientific practice, and relationships between paradigms of inquiry and scientific fraud. The next part considers the practice of social science discovery. Chapters discuss the lack of genuine scientific discovery in finance where hypotheses concern the cheapness of securities, the logic of scientific discovery in macroeconomics, and the nature of that what discovery with the Solidarity movement as a case study. The final part covers formalising theories in social science. Chapters analyse the abstract model theory of institutions as a way of representing the structure of scientific theories, the semi-automatic generation of cognitive science theories, and computational process models in the social sciences. The volume offers a unique perspective on scientific discovery in the social sciences. It will engage scholars and students with a multidisciplinary interest in the philosophy of science and social science. |
| definition of prediction in science: Inquiry and the National Science Education Standards National Research Council, Center for Science, Mathematics, and Engineering Education, Committee on Development of an Addendum to the National Science Education Standards on Scientific Inquiry, 2000-05-03 Humans, especially children, are naturally curious. Yet, people often balk at the thought of learning scienceâ€the eyes glazed over syndrome. Teachers may find teaching science a major challenge in an era when science ranges from the hardly imaginable quark to the distant, blazing quasar. Inquiry and the National Science Education Standards is the book that educators have been waiting forâ€a practical guide to teaching inquiry and teaching through inquiry, as recommended by the National Science Education Standards. This will be an important resource for educators who must help school boards, parents, and teachers understand why we can't teach the way we used to. Inquiry refers to the diverse ways in which scientists study the natural world and in which students grasp science knowledge and the methods by which that knowledge is produced. This book explains and illustrates how inquiry helps students learn science content, master how to do science, and understand the nature of science. This book explores the dimensions of teaching and learning science as inquiry for K-12 students across a range of science topics. Detailed examples help clarify when teachers should use the inquiry-based approach and how much structure, guidance, and coaching they should provide. The book dispels myths that may have discouraged educators from the inquiry-based approach and illuminates the subtle interplay between concepts, processes, and science as it is experienced in the classroom. Inquiry and the National Science Education Standards shows how to bring the standards to life, with features such as classroom vignettes exploring different kinds of inquiries for elementary, middle, and high school and Frequently Asked Questions for teachers, responding to common concerns such as obtaining teaching supplies. Turning to assessment, the committee discusses why assessment is important, looks at existing schemes and formats, and addresses how to involve students in assessing their own learning achievements. In addition, this book discusses administrative assistance, communication with parents, appropriate teacher evaluation, and other avenues to promoting and supporting this new teaching paradigm. |
| definition of prediction in science: Modeling of Transport Demand V.A Profillidis, G.N. Botzoris, 2018-10-23 Modeling of Transport Demand explains the mechanisms of transport demand, from analysis to calculation and forecasting. Packed with strategies for forecasting future demand for all transport modes, the book helps readers assess the validity and accuracy of demand forecasts. Forecasting and evaluating transport demand is an essential task of transport professionals and researchers that affects the design, extension, operation, and maintenance of all transport infrastructures. Accurate demand forecasts are necessary for companies and government entities when planning future fleet size, human resource needs, revenues, expenses, and budgets. The operational and planning skills provided in Modeling of Transport Demand help readers solve the problems they face on a daily basis. Modeling of Transport Demand is written for researchers, professionals, undergraduate and graduate students at every stage in their careers, from novice to expert. The book assists those tasked with constructing qualitative models (based on executive judgment, Delphi, scenario writing, survey methods) or quantitative ones (based on statistical, time series, econometric, gravity, artificial neural network, and fuzzy methods) in choosing the most suitable solution for all types of transport applications. - Presents the most recent and relevant findings and research - both at theoretical and practical levels - of transport demand - Provides a theoretical analysis and formulations that are clearly presented for ease of understanding - Covers analysis for all modes of transportation - Includes case studies that present the most appropriate formulas and methods for finding solutions and evaluating results |
| definition of prediction in science: Philosophy and Technology Carl Mitcham, Robert Mackey, 1972 Philosophy and technology is a comprehensive collection of selected readings treating technology as a general philosophical problem. Theses essays, by such eminent philosophers as Lewis Mumford, Jacques Ellul, José Ortega y Gasset, and Friedrich Dessauer, are divided into five major categories: conceptus issues, ethical and political critiques, religious critiques, existential critiques, and metaphysical studies. Each of these essays present an in-depth analysis of major arguments and ideas relevant to the particular area and is designed to bring out opposing viewpoints. The essays span the period from 1927 to the present. Read chronologically, they trace the development of the philosophy of technology as a specific discipline....Philosophy and Technology will serve as excellent source material for undergraduate and graduate students interested in this field as well as in political philosophy, philosophy of science, philosophy of religion, epistemology, and metaphysics -- |
| definition of prediction in science: From So Simple a Beginning Charles Darwin, 2010-08-31 Hailed as superior by Nature, this landmark volume is available in a collectible, boxed edition. Never before have the four great works of Charles Darwin—Voyage of the H.M.S. Beagle (1845), The Origin of Species (1859), The Descent of Man (1871), and The Expression of Emotions in Man and Animals (1872)—been collected under one cover. Undertaking this challenging endeavor 123 years after Darwin's death, two-time Pulitzer Prize winner Edward O. Wilson has written an introductory essay for the occasion, while providing new, insightful introductions to each of the four volumes and an afterword that examines the fate of evolutionary theory in an era of religious resistance. In addition, Wilson has crafted a creative new index to accompany these four texts, which links the nineteenth-century, Darwinian evolutionary concepts to contemporary biological thought. Beautifully slipcased, and including restored versions of the original illustrations, From So Simple a Beginning turns our attention to the astounding power of the natural creative process and the magnificence of its products. |
| definition of prediction in science: Economic Value of Weather and Climate Forecasts Richard W. Katz, Allan H. Murphy, 1997 Weather and climate extremes can significantly impact the economics of a region. This book examines how weather and climate forecasts can be used to mitigate the impact of the weather on the economy. Interdisciplinary in scope, it explores the meteorological, economic, psychological, and statistical aspects to weather prediction. The contributors encompass forecasts over a wide range of temporal scales, from weather over the next few hours to the climate months or seasons ahead, and address the impact of these forecasts on human behaviour. Economic Value of Weather and Climate Forecasts seeks to determine the economic benefits of existing weather forecasting systems and the incremental benefits of improving these systems, and will be an interesting and essential reference for economists, statisticians, and meteorologists. |
| definition of prediction in science: Patterns, Predictions, and Actions: Foundations of Machine Learning Moritz Hardt, Benjamin Recht, 2022-08-23 An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers |
| definition of prediction in science: The Structure of Scientific Revolutions Thomas S. Kuhn, 1969 |
| definition of prediction in science: Philosophy of Epidemiology A. Broadbent, 2013-06-10 Epidemiology is one of the fastest growing and increasingly important sciences. This thorough analysis lays out the conceptual foundations of epidemiology, identifying traps and setting out the benefits of properly understanding this fascinating and important discipline, as well as providing the means to do so. |
| definition of prediction in science: The Great Mental Models, Volume 1 Shane Parrish, Rhiannon Beaubien, 2024-10-15 Discover the essential thinking tools you’ve been missing with The Great Mental Models series by Shane Parrish, New York Times bestselling author and the mind behind the acclaimed Farnam Street blog and “The Knowledge Project” podcast. This first book in the series is your guide to learning the crucial thinking tools nobody ever taught you. Time and time again, great thinkers such as Charlie Munger and Warren Buffett have credited their success to mental models–representations of how something works that can scale onto other fields. Mastering a small number of mental models enables you to rapidly grasp new information, identify patterns others miss, and avoid the common mistakes that hold people back. The Great Mental Models: Volume 1, General Thinking Concepts shows you how making a few tiny changes in the way you think can deliver big results. Drawing on examples from history, business, art, and science, this book details nine of the most versatile, all-purpose mental models you can use right away to improve your decision making and productivity. This book will teach you how to: Avoid blind spots when looking at problems. Find non-obvious solutions. Anticipate and achieve desired outcomes. Play to your strengths, avoid your weaknesses, … and more. The Great Mental Models series demystifies once elusive concepts and illuminates rich knowledge that traditional education overlooks. This series is the most comprehensive and accessible guide on using mental models to better understand our world, solve problems, and gain an advantage. |
| definition of prediction in science: Security Science Clifton Smith, David J Brooks, 2012-12-31 Security Science integrates the multi-disciplined practice areas of security into a single structured body of knowledge, where each chapter takes an evidence-based approach to one of the core knowledge categories. The authors give practitioners and students the underlying scientific perspective based on robust underlying theories, principles, models or frameworks. Demonstrating the relationships and underlying concepts, they present an approach to each core security function within the context of both organizational security and homeland security. The book is unique in its application of the scientific method to the increasingly challenging tasks of preventing crime and foiling terrorist attacks. Incorporating the latest security theories and principles, it considers security from both a national and corporate perspective, applied at a strategic and tactical level. It provides a rational basis for complex decisions and begins the process of defining the emerging discipline of security science. - A fresh and provocative approach to the key facets of security - Presentation of theories and models for a reasoned approach to decision making - Strategic and tactical support for corporate leaders handling security challenges - Methodologies for protecting national assets in government and private sectors - Exploration of security's emerging body of knowledge across domains |
| definition of prediction in science: Superforecasting Philip E. Tetlock, Dan Gardner, 2015-09-29 NEW YORK TIMES BESTSELLER • NAMED ONE OF THE BEST BOOKS OF THE YEAR BY THE ECONOMIST “The most important book on decision making since Daniel Kahneman's Thinking, Fast and Slow.”—Jason Zweig, The Wall Street Journal Everyone would benefit from seeing further into the future, whether buying stocks, crafting policy, launching a new product, or simply planning the week’s meals. Unfortunately, people tend to be terrible forecasters. As Wharton professor Philip Tetlock showed in a landmark 2005 study, even experts’ predictions are only slightly better than chance. However, an important and underreported conclusion of that study was that some experts do have real foresight, and Tetlock has spent the past decade trying to figure out why. What makes some people so good? And can this talent be taught? In Superforecasting, Tetlock and coauthor Dan Gardner offer a masterwork on prediction, drawing on decades of research and the results of a massive, government-funded forecasting tournament. The Good Judgment Project involves tens of thousands of ordinary people—including a Brooklyn filmmaker, a retired pipe installer, and a former ballroom dancer—who set out to forecast global events. Some of the volunteers have turned out to be astonishingly good. They’ve beaten other benchmarks, competitors, and prediction markets. They’ve even beaten the collective judgment of intelligence analysts with access to classified information. They are superforecasters. In this groundbreaking and accessible book, Tetlock and Gardner show us how we can learn from this elite group. Weaving together stories of forecasting successes (the raid on Osama bin Laden’s compound) and failures (the Bay of Pigs) and interviews with a range of high-level decision makers, from David Petraeus to Robert Rubin, they show that good forecasting doesn’t require powerful computers or arcane methods. It involves gathering evidence from a variety of sources, thinking probabilistically, working in teams, keeping score, and being willing to admit error and change course. Superforecasting offers the first demonstrably effective way to improve our ability to predict the future—whether in business, finance, politics, international affairs, or daily life—and is destined to become a modern classic. |
| definition of prediction in science: The Barefoot Emperor Philip Marsden, 2007 A fascinating narrative excursion into a bizarre episode in 19th century Ethiopian and British imperial history, featuring a remote African despot and his monstrous European-built gun. Towards the end of 1867, Emperor Tewodros II of Ethiopia burnt his own capital, took his vast mortar a named 'Sevastopol' and began a retreat to the mountain stronghold of Mekdala. For months thousands of his followers struggled to build a road for the great gun, levelling the soil of the high plains, hacking out a way down into mile-deep gorges. At the same time, a hostile British force, under General Napier, was advancing from the coast. It was the climax to the reign of one of the most colourful and extraordinary rulers in African history. Discovering traces of the road in the highlands, and drawing on years of involvement with Ethiopia, Philip Marsden recounts the story of Tewodros. From his spectacular rise - from camel-raider to King of Kings - Twewodros was a man who combined a sense of Biblical destiny with personal charisma and military genius. He restored the fortunes of the ancient Christian kingdom, introduced reforms to his army and to the church, and dreamed of an alliance with the great powers of Europe. But as his reforms stalled and the British Foreign Office lost his letter to Queen Victoria, Tewodros's behaviour became more and more violent and erratic. When he imprisoned the British consul, years of negotiation culminated in one of the most bizarre - and expensive - campaigns of the Victorian age. 'The Barefoot Emperor' is history at its most thrilling and dramatic. Using narrative skills proven in such acclaimed books as 'The Bronski House' and 'The Chains of Heaven', Philip Marsden recreates scenes and characters of glittering intensity - and the intriguing paradoxes of a central figure grappling not only with his own people and his own demons, but with the seductive and unstoppable approach of the modern world. |
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A definition is a statement giving the meaning of a word or expression, especially in a dictionary.
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Definition of definition noun in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage …
DEFINITION Definition & Meaning - Merriam-Webster
The meaning of DEFINITION is a statement of the meaning of a word or word group or a sign or symbol. How to use definition in a sentence.
DEFINITION Definition & Meaning - Dictionary.com
Definition definition: the act of defining, or of making something definite, distinct, or clear.. See examples of DEFINITION used in a sentence.
DEFINITION | English meaning - Cambridge Dictionary
DEFINITION definition: 1. a statement that explains the meaning of a word or phrase: 2. a description of the features and…. Learn more.
DEFINITION definition and meaning | Collins English Dictionary
A definition is a statement giving the meaning of a word or expression, especially in a dictionary.
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Definition of definition noun in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
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Definition Definition & Meaning | Britannica Dictionary
DEFINITION meaning: 1 : an explanation of the meaning of a word, phrase, etc. a statement that defines a word, phrase, etc.; 2 : a statement that describes what something is
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