Foundational Principles For Large Scale Inference

Author : National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Board on Mathematical Sciences and Their Applications,Committee on Applied and Theoretical Statistics
Publisher : National Academies Press
Page : 115 pages
File Size : 49,6 Mb
Release : 2017-02-24
Category : Mathematics
ISBN : 9780309454476

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Refining the Concept of Scientific Inference When Working with Big Data by National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Board on Mathematical Sciences and Their Applications,Committee on Applied and Theoretical Statistics Pdf

The concept of utilizing big data to enable scientific discovery has generated tremendous excitement and investment from both private and public sectors over the past decade, and expectations continue to grow. Using big data analytics to identify complex patterns hidden inside volumes of data that have never been combined could accelerate the rate of scientific discovery and lead to the development of beneficial technologies and products. However, producing actionable scientific knowledge from such large, complex data sets requires statistical models that produce reliable inferences (NRC, 2013). Without careful consideration of the suitability of both available data and the statistical models applied, analysis of big data may result in misleading correlations and false discoveries, which can potentially undermine confidence in scientific research if the results are not reproducible. In June 2016 the National Academies of Sciences, Engineering, and Medicine convened a workshop to examine critical challenges and opportunities in performing scientific inference reliably when working with big data. Participants explored new methodologic developments that hold significant promise and potential research program areas for the future. This publication summarizes the presentations and discussions from the workshop.

Author : Olusola O. Adesope,A.G. Rud
Publisher : Springer
Page : 268 pages
File Size : 40,9 Mb
Release : 2018-11-08
Category : Education
ISBN : 9783319896809

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Contemporary Technologies in Education by Olusola O. Adesope,A.G. Rud Pdf

This edited volume provides a critical discussion of theoretical, methodological, and practical developments of contemporary forms of educational technologies. Specifically, the book discusses the use of contemporary technologies such as the Flipped Classroom (FC), Massive Open Online Course (MOOC), Social Media, Serious Educational Games (SEG), Wikis, innovative learning software tools, and learning analytic approach for making sense of big data. While some of these contemporary educational technologies have been touted as panaceas, researchers and developers have been faced with enormous challenges in enhancing the use of these technologies to arouse student attention and improve persistent motivation, engagement, and learning. Hence, the book examines how contemporary technologies can engender student motivation and result in improved engagement and learning. Each chapter also discusses the road ahead and where appropriate, uses the current trend to predict future affordances of technologies.

Author : Bradley Efron
Publisher : Unknown
Page : 276 pages
File Size : 52,6 Mb
Release : 2010
Category : Electronic
ISBN : OCLC:1137347321

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Large-Scale Inference by Bradley Efron Pdf

We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and frequentist ideas. Estimation, testing and prediction blend in this framework, producing opportunities for new methodologies of increased power. New difficulties also arise, easily leading to flawed inferences. This book takes a careful look at both the promise and pitfalls of large-scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Emphasis is on the inferential ideas underlying technical developments, illustrated using a large number of real examples.

Author : Yuhui Du,Jing Sui,Dongdong Lin
Publisher : Frontiers Media SA
Page : 288 pages
File Size : 48,8 Mb
Release : 2020-07-10
Category : Electronic
ISBN : 9782889634040

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Identifying Neuroimaging-Based Markers for Distinguishing Brain Disorders by Yuhui Du,Jing Sui,Dongdong Lin Pdf

There has been increasing interests in exploring biomarkers from brain images, aiming to have a better understanding and a more effective diagnosis of brain disorders such as schizophrenia, bipolar disorder, schizoaffective disorder, autism spectrum disorder, attention-deficit/hyperactivity disorder, Alzheimer’s disease and so on. Therefore, it is important to identify disease-specific changes for distinguishing healthy controls and patients with brain disorders as well as for differentiating patients with different disorders showing similar clinical symptoms. Biomarkers can be identified from different types of brain Imaging techniques including functional magnetic resonance imaging (fMRI), structural MRI, positron emission tomography (PET), electroencephalography (EEG), and magnetoencephalography (MEG) by using statistical analysis methods. Furthermore, based on measures from brain imaging techniques, machine learning techniques can help to classify or predict disease for individual subjects. In fact, fusion of features from multiple modalities may benefit the understanding of disease mechanism and improve the classification performance. This Research Topic further explores the functional or structural alterations in brain disorders.

Author : Bradley Efron
Publisher : Cambridge University Press
Page : 128 pages
File Size : 54,5 Mb
Release : 2012-11-29
Category : Mathematics
ISBN : 9781139492133

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Large-Scale Inference by Bradley Efron Pdf

We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and frequentist ideas. Estimation, testing and prediction blend in this framework, producing opportunities for new methodologies of increased power. New difficulties also arise, easily leading to flawed inferences. This book takes a careful look at both the promise and pitfalls of large-scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Emphasis is on the inferential ideas underlying technical developments, illustrated using a large number of real examples.

Author : Robert C. Qiu,Paul Antonik
Publisher : John Wiley & Sons
Page : 626 pages
File Size : 42,7 Mb
Release : 2017-04-17
Category : Technology & Engineering
ISBN : 9781118494059

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Smart Grid using Big Data Analytics by Robert C. Qiu,Paul Antonik Pdf

This book is aimed at students in communications and signal processing who want to extend their skills in the energy area. It describes power systems and why these backgrounds are so useful to smart grid, wireless communications being very different to traditional wireline communications.

Author : Xinping Cui,Thorsten Dickhaus,Ying Ding,Jason C. Hsu
Publisher : CRC Press
Page : 418 pages
File Size : 50,9 Mb
Release : 2021-11-18
Category : Mathematics
ISBN : 9780429633881

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Handbook of Multiple Comparisons by Xinping Cui,Thorsten Dickhaus,Ying Ding,Jason C. Hsu Pdf

Written by experts that include originators of some key ideas, chapters in the Handbook of Multiple Testing cover multiple comparison problems big and small, with guidance toward error rate control and insights on how principles developed earlier can be applied to current and emerging problems. Some highlights of the coverages are as follows. Error rate control is useful for controlling the incorrect decision rate. Chapter 1 introduces Tukey's original multiple comparison error rates and point to how they have been applied and adapted to modern multiple comparison problems as discussed in the later chapters. Principles endure. While the closed testing principle is more familiar, Chapter 4 shows the partitioning principle can derive confidence sets for multiple tests, which may become important as the profession goes beyond making decisions based on p-values. Multiple comparisons of treatment efficacy often involve multiple doses and endpoints. Chapter 12 on multiple endpoints explains how different choices of endpoint types lead to different multiplicity adjustment strategies, while Chapter 11 on the MCP-Mod approach is particularly useful for dose-finding. To assess efficacy in clinical trials with multiple doses and multiple endpoints, the reader can see the traditional approach in Chapter 2, the Graphical approach in Chapter 5, and the multivariate approach in Chapter 3. Personalized/precision medicine based on targeted therapies, already a reality, naturally leads to analysis of efficacy in subgroups. Chapter 13 draws attention to subtle logical issues in inferences on subgroups and their mixtures, with a principled solution that resolves these issues. This chapter has implication toward meeting the ICHE9R1 Estimands requirement. Besides the mere multiple testing methodology itself, the handbook also covers related topics like the statistical task of model selection in Chapter 7 or the estimation of the proportion of true null hypotheses (or, in other words, the signal prevalence) in Chapter 8. It also contains decision-theoretic considerations regarding the admissibility of multiple tests in Chapter 6. The issue of selected inference is addressed in Chapter 9. Comparison of responses can involve millions of voxels in medical imaging or SNPs in genome-wide association studies (GWAS). Chapter 14 and Chapter 15 provide state of the art methods for large scale simultaneous inference in these settings.

Author : Ben Goertzel,Matthew Iklé,Izabela Freire Goertzel,Ari Heljakka
Publisher : Springer Science & Business Media
Page : 331 pages
File Size : 40,8 Mb
Release : 2008-12-16
Category : Computers
ISBN : 9780387768724

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Probabilistic Logic Networks by Ben Goertzel,Matthew Iklé,Izabela Freire Goertzel,Ari Heljakka Pdf

Abstract In this chapter we provide an overview of probabilistic logic networks (PLN), including our motivations for developing PLN and the guiding principles underlying PLN. We discuss foundational choices we made, introduce PLN knowledge representation, and briefly introduce inference rules and truth-values. We also place PLN in context with other approaches to uncertain inference. 1.1 Motivations This book presents Probabilistic Logic Networks (PLN), a systematic and pragmatic framework for computationally carrying out uncertain reasoning – r- soning about uncertain data, and/or reasoning involving uncertain conclusions. We begin with a few comments about why we believe this is such an interesting and important domain of investigation. First of all, we hold to a philosophical perspective in which “reasoning” – properly understood – plays a central role in cognitive activity. We realize that other perspectives exist; in particular, logical reasoning is sometimes construed as a special kind of cognition that humans carry out only occasionally, as a deviation from their usual (intuitive, emotional, pragmatic, sensorimotor, etc.) modes of thought. However, we consider this alternative view to be valid only according to a very limited definition of “logic.” Construed properly, we suggest, logical reasoning may be understood as the basic framework underlying all forms of cognition, including those conventionally thought of as illogical and irrational.

Author : Miltiadis C. Mavrakakis,Jeremy Penzer
Publisher : CRC Press
Page : 444 pages
File Size : 49,8 Mb
Release : 2021-03-28
Category : Mathematics
ISBN : 9781315362045

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Probability and Statistical Inference by Miltiadis C. Mavrakakis,Jeremy Penzer Pdf

Probability and Statistical Inference: From Basic Principles to Advanced Models covers aspects of probability, distribution theory, and inference that are fundamental to a proper understanding of data analysis and statistical modelling. It presents these topics in an accessible manner without sacrificing mathematical rigour, bridging the gap between the many excellent introductory books and the more advanced, graduate-level texts. The book introduces and explores techniques that are relevant to modern practitioners, while being respectful to the history of statistical inference. It seeks to provide a thorough grounding in both the theory and application of statistics, with even the more abstract parts placed in the context of a practical setting. Features: •Complete introduction to mathematical probability, random variables, and distribution theory. •Concise but broad account of statistical modelling, covering topics such as generalised linear models, survival analysis, time series, and random processes. •Extensive discussion of the key concepts in classical statistics (point estimation, interval estimation, hypothesis testing) and the main techniques in likelihood-based inference. •Detailed introduction to Bayesian statistics and associated topics. •Practical illustration of some of the main computational methods used in modern statistical inference (simulation, boostrap, MCMC). This book is for students who have already completed a first course in probability and statistics, and now wish to deepen and broaden their understanding of the subject. It can serve as a foundation for advanced undergraduate or postgraduate courses. Our aim is to challenge and excite the more mathematically able students, while providing explanations of statistical concepts that are more detailed and approachable than those in advanced texts. This book is also useful for data scientists, researchers, and other applied practitioners who want to understand the theory behind the statistical methods used in their fields.

Author : United States. Congress. House. Committee on Appropriations. Subcommittee on Commerce, Justice, Science, and Related Agencies
Publisher : Unknown
Page : 1180 pages
File Size : 53,5 Mb
Release : 2013
Category : Administrative agencies
ISBN : IND:30000144688524

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Commerce, Justice, Science, and Related Agencies Appropriations for 2014 by United States. Congress. House. Committee on Appropriations. Subcommittee on Commerce, Justice, Science, and Related Agencies Pdf

Author : Marielle Simon,Kadriye Ercikan,Michel Rousseau
Publisher : Routledge
Page : 320 pages
File Size : 52,8 Mb
Release : 2012-08-21
Category : Education
ISBN : 9781136578359

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Improving Large-Scale Assessment in Education by Marielle Simon,Kadriye Ercikan,Michel Rousseau Pdf

Large-scale assessments (LSAs) play a growing role in education policy decisions, accountability, and education planning worldwide. This book focuses on central issues that are key components of successful planning, development and implementation of LSAs. The book’s main distinction is its focus on practice- based, cutting-edge research. This is achieved by having chapters co-authored by world-class researchers in collaboration with measurement practitioners. The result is a how-to book whose language is accessible to practitioners and graduate students as well as academics. No other book so thoroughly covers current issues in the field of large-scale assessment. An introductory chapter is followed by sixteen chapters that each focus on a specific issue. The content is prescriptive and didactic in nature but based on the most recent scientific research. It includes successful experiences, exemplary practices, training modules, interesting breakthroughs or alternatives, and promising innovations regarding large-scale assessments. Finally, it covers meaningful topics that are currently taking center stage such as motivating students, background questionnaires, comparability of different linguistic versions of assessments, and cognitive modeling of learning and assessment.

Author : Henry E. Kyburg Jr.
Publisher : Springer Science & Business Media
Page : 440 pages
File Size : 50,8 Mb
Release : 2012-12-06
Category : Philosophy
ISBN : 9789401021753

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The Logical Foundations of Statistical Inference by Henry E. Kyburg Jr. Pdf

Everyone knows it is easy to lie with statistics. It is important then to be able to tell a statistical lie from a valid statistical inference. It is a relatively widely accepted commonplace that our scientific knowledge is not certain and incorrigible, but merely probable, subject to refinement, modifi cation, and even overthrow. The rankest beginner at a gambling table understands that his decisions must be based on mathematical ex pectations - that is, on utilities weighted by probabilities. It is widely held that the same principles apply almost all the time in the game of life. If we turn to philosophers, or to mathematical statisticians, or to probability theorists for criteria of validity in statistical inference, for the general principles that distinguish well grounded from ill grounded generalizations and laws, or for the interpretation of that probability we must, like the gambler, take as our guide in life, we find disagreement, confusion, and frustration. We might be prepared to find disagreements on a philosophical and theoretical level (although we do not find them in the case of deductive logic) but we do not expect, and we may be surprised to find, that these theoretical disagreements lead to differences in the conclusions that are regarded as 'acceptable' in the practice of science and public affairs, and in the conduct of business.

Author : Dolores Albarracin,Blair T. Johnson
Publisher : Routledge
Page : 664 pages
File Size : 48,8 Mb
Release : 2018-10-10
Category : Business & Economics
ISBN : 9781351712408

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The Handbook of Attitudes, Volume 1: Basic Principles by Dolores Albarracin,Blair T. Johnson Pdf

Attitudes are evaluations of people, places, things, and ideas. They help us to navigate through a complex world. They provide guidance for decisions about which products to buy, how to travel to work, or where to go on vacation. They color our perceptions of others. Carefully crafted interventions can change attitudes and behavior. Yet, attitudes, beliefs, and behavior are often formed and changed in casual social exchanges. The mere perception that other people favor something, say, rich people, may be sufficient to make another person favor it. People’s own actions also influence their attitudes, such that they adjust to be more supportive of the actions. People’s belief systems even change to align with and support their preferences, which at its extreme is a form of denial for which people lack awareness. These two volumes provide authoritative, critical surveys of theory and research about attitudes, beliefs, persuasion, and behavior from key authors in these areas. The first volume covers theoretical notions about attitudes, the beliefs and behaviors to which they are linked, and the degree to which they are held outside of awareness. It also discusses motivational and cultural determinants of attitudes, influences of attitudes on behavior, and communication and persuasion. The second volume covers applications to measurement, behavior prediction, and interventions in the areas of cancer, HIV, substance use, diet, and exercise, as well as in politics, intergroup relations, aggression, migrations, advertising, accounting, education, and the environment.

Author : Stefano Boccaletti,Vito Latora,Yamir Moreno
Publisher : World Scientific
Page : 465 pages
File Size : 43,8 Mb
Release : 2010
Category : Science
ISBN : 9789812838797

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Handbook on Biological Networks by Stefano Boccaletti,Vito Latora,Yamir Moreno Pdf

Networked systems are all around us. The accumulated evidence of systems as complex as a cell cannot be fully understood by studying only their isolated constituents, giving rise to a new area of interest in research ? the study of complex networks. In a broad sense, biological networks have been one of the most studied networks, and the field has benefited from many important contributions. By understanding and modeling the structure of a biological network, a better perception of its dynamical and functional behavior is to be expected. This unique book compiles the most relevant results and novel insights provided by network theory in the biological sciences, ranging from the structure and dynamics of the brain to cellular and protein networks and to population-level biology.

Author : D. R. Cox
Publisher : Cambridge University Press
Page : 227 pages
File Size : 43,5 Mb
Release : 2006-08-10
Category : Mathematics
ISBN : 9781139459136

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Principles of Statistical Inference by D. R. Cox Pdf

In this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of statistical inference. He develops the key concepts, describing and comparing the main ideas and controversies over foundational issues that have been keenly argued for more than two-hundred years. Continuing a sixty-year career of major contributions to statistical thought, no one is better placed to give this much-needed account of the field. An appendix gives a more personal assessment of the merits of different ideas. The content ranges from the traditional to the contemporary. While specific applications are not treated, the book is strongly motivated by applications across the sciences and associated technologies. The mathematics is kept as elementary as feasible, though previous knowledge of statistics is assumed. The book will be valued by every user or student of statistics who is serious about understanding the uncertainty inherent in conclusions from statistical analyses.