Author : Macartan Humphreys
Publisher : Cambridge University Press
Page : 435 pages
File Size : 48,59 MB
Release : 2023-10-31
Category : Social Science
ISBN : 1107169623
Develops a new approach to the use of causal models for qualitative and mixed-method research design and causal inference.
Author : George E. P. Box
Publisher : John Wiley & Sons
Page : 610 pages
File Size : 44,74 MB
Release : 2011-01-25
Category : Mathematics
ISBN : 111803144X
Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach.
Author : D.A. Sprott
Publisher : Springer Science & Business Media
Page : 254 pages
File Size : 24,26 MB
Release : 2008-01-28
Category : Mathematics
ISBN : 0387227660
A treatment of the problems of inference associated with experiments in science, with the emphasis on techniques for dividing the sample information into various parts, such that the diverse problems of inference that arise from repeatable experiments may be addressed. A particularly valuable feature is the large number of practical examples, many of which use data taken from experiments published in various scientific journals. This book evolved from the authors own courses on statistical inference, and assumes an introductory course in probability, including the calculation and manipulation of probability functions and density functions, transformation of variables and the use of Jacobians. While this is a suitable text book for advanced undergraduate, Masters, and Ph.D. statistics students, it may also be used as a reference book.
Author : A. H. Welsh
Publisher : John Wiley & Sons
Page : 480 pages
File Size : 23,91 MB
Release : 2011-09-15
Category : Mathematics
ISBN : 1118165438
Relevant, concrete, and thorough--the essential data-based text onstatistical inference The ability to formulate abstract concepts and draw conclusionsfrom data is fundamental to mastering statistics. Aspects ofStatistical Inference equips advanced undergraduate and graduatestudents with a comprehensive grounding in statistical inference,including nonstandard topics such as robustness, randomization, andfinite population inference. A. H. Welsh goes beyond the standard texts and expertly synthesizesbroad, critical theory with concrete data and relevant topics. Thetext follows a historical framework, uses real-data sets andstatistical graphics, and treats multiparameter problems, yet isultimately about the concepts themselves. Written with clarity and depth, Aspects of Statistical Inference: * Provides a theoretical and historical grounding in statisticalinference that considers Bayesian, fiducial, likelihood, andfrequentist approaches * Illustrates methods with real-data sets on diabetic retinopathy,the pharmacological effects of caffeine, stellar velocity, andindustrial experiments * Considers multiparameter problems * Develops large sample approximations and shows how to use them * Presents the philosophy and application of robustness theory * Highlights the central role of randomization in statistics * Uses simple proofs to illuminate foundational concepts * Contains an appendix of useful facts concerning expansions,matrices, integrals, and distribution theory Here is the ultimate data-based text for comparing and presentingthe latest approaches to statistical inference.
Author : Alan Blackwell
Publisher : Springer Science & Business Media
Page : 469 pages
File Size : 18,87 MB
Release : 2004-03-12
Category : Art
ISBN : 354021268X
This book constitutes the refereed proceedings of the Third International Conference, Diagrams 2004, held in Cambridge, UK, in March 2004. The 18 revised full papers and 42 revised poster papers presented together with a survey article and the abstracts of 2 posters were carefully reviewed and selected from a total of 91 submissions. The papers are organized in topical sections on fundamental issues, logical aspects of diagrammatic representation and reasoning, computational aspects of diagrammatic representation and reasoning, cognitive aspects of diagrammatic representation and reasoning, visualizing information with diagrams, diagrams in human-computer interaction, and diagrams in software engineering.
Author : Kate Cain
Publisher : John Wiley & Sons
Page : 276 pages
File Size : 23,74 MB
Release : 2010-06-21
Category : Psychology
ISBN : 1405151552
Reading Development and Difficulties is a comprehensive and balanced introduction to the development of the two core aspects of reading: good word reading skills and the ability to extract the overall meaning of a text. Unique in its balanced coverage of both word reading and reading comprehension development, this book is an essential resource for undergraduates studying literacy acquisition Offers wide coverage of the subject and discusses both typical development and the development of difficulties in reading Accessibly written for students and professionals with no previous background in reading development or reading difficulties Provides a detailed examination of the specific problems that underlie reading difficulties
Author : Edward J. O'Brien
Publisher : Cambridge University Press
Page : 439 pages
File Size : 37,97 MB
Release : 2015-04-16
Category : Education
ISBN : 1107049792
A study of inferencing from a wide variety of theoretical and disciplinary perspectives, as well as different levels of processing.
Author : Philip C. Short
Publisher : IAP
Page : 487 pages
File Size : 34,8 MB
Release : 2021-12-01
Category : Education
ISBN : 1648027997
In an age where we are inundated with information, the ability to discern verifiable information to make proper decisions and solve problems is ever more critical. Modern science, which espouses a systematic approach to making “inferences,” requires a certain mindset that allows for a degree of comfort with uncertainty. This book offers inspirations and ideas for cultivating the proper mindset for the studying, teaching, and practicing of science that will be useful for those new to as well as familiar with the field. Although a paradigm shift from traditional instruction is suggested in the National Framework for K-12 science, this volume is intended to help educators develop a personal mental framework in which to transition from a teacher-centered, didactical approach to a student-centered, evidence-guided curriculum. While the topics of the book derive from currently published literature on STEM education as they relate to the National Framework for K-12 Science and the Three-Dimensional science instruction embedded in the Next Generation Science Standards, this book also examines these topics in the context of a new societal age posited as the “Age of Inference” and addresses how to make sense of the ever-increasing deluge of information that we are experiencing by having a scientific and properly discerning mindset. ENDORSEMENTS: "This volume takes on one of the thorniest existential problems of our time, the contradiction between the exponentially growing amount of information that individuals have access to, and the diminished capacity of those individuals to understand it. Its chapters provide the reader with an introduction to the relationship between knowledge, science, and inference; needed new approaches to learning science in our new data rich world; and a discussion of what we can and must do to reduce or eliminate the growing gap between the inference have’s and have nots. It is not too much to say that how we resolve the issues outlined in this volume will determine the future of our species on this planet." — Joseph L. Graves Jr., Professor of Biological Sciences North Carolina A&T State University, Fellow, American Association for the Advancement of Science: Biological Sciences, Author of: The Emperor’s New Clothes: Biological Theories of Race at the Millennium "Big data is not enough for addressing dangers to the environment or tackling threats to democracy; we need the ability to draw sound inferences from the data. Cultivating a scientific mindset requires fundamental changes to the way we teach and learn. This important and well -written volume shows how." — Ashok Goel, Professor of Computer Science and Human Centered Computing, Georgia Institute of Technology. Editor of AI Magazine Founding Editor of AAAI’s Interactive AI Magazine "If you are a science teacher concerned about the implications of information overload, analysis paralysis, and intellectual complacency on our health, economic future, and democracy, then I recommend this book." — Michael Svec, Professor for Physics and Astronomy Education, Furman University, Fulbright Scholar to Czech Republic
Author : Benjamin Haibe-Kains
Publisher : Frontiers Media SA
Page : 192 pages
File Size : 34,16 MB
Release : 2015-04-14
Category : Bioengineering
ISBN : 2889194787
Scientists today have access to an unprecedented arsenal of high-tech tools that can be used to thoroughly characterize biological systems of interest. High-throughput “omics” technologies enable to generate enormous quantities of data at the DNA, RNA, epigenetic and proteomic levels. One of the major challenges of the post-genomic era is to extract functional information by integrating such heterogeneous high-throughput genomic data. This is not a trivial task as we are increasingly coming to understand that it is not individual genes, but rather biological pathways and networks that drive an organism’s response to environmental factors and the development of its particular phenotype. In order to fully understand the way in which these networks interact (or fail to do so) in specific states (disease for instance), we must learn both, the structure of the underlying networks and the rules that govern their behavior. In recent years there has been an increasing interest in methods that aim to infer biological networks. These methods enable the opportunity for better understanding the interactions between genomic features and the overall structure and behavior of the underlying networks. So far, such network models have been mainly used to identify and validate new interactions between genes of interest. But ultimately, one could use these networks to predict large-scale effects of perturbations, such as treatment by multiple targeted drugs. However, currently, we are still at an early stage of comprehending methods and approaches providing a robust statistical framework to quantitatively assess the quality of network inference and its predictive potential. The scope of this Research Topic in Bioinformatics and Computational Biology aims at addressing these issues by investigating the various, complementary approaches to quantify the quality of network models. These “validation” techniques could focus on assessing quality of specific interactions, global and local structures, and predictive ability of network models. These methods could rely exclusively on in silico evaluation procedures or they could be coupled with novel experimental designs to generate the biological data necessary to properly validate inferred networks.
Author : Helio S. Migon
Publisher : CRC Press
Page : 388 pages
File Size : 45,68 MB
Release : 2014-09-03
Category : Mathematics
ISBN : 1439878803
A Balanced Treatment of Bayesian and Frequentist Inference Statistical Inference: An Integrated Approach, Second Edition presents an account of the Bayesian and frequentist approaches to statistical inference. Now with an additional author, this second edition places a more balanced emphasis on both perspectives than the first edition. New to the Second Edition New material on empirical Bayes and penalized likelihoods and their impact on regression models Expanded material on hypothesis testing, method of moments, bias correction, and hierarchical models More examples and exercises More comparison between the approaches, including their similarities and differences Designed for advanced undergraduate and graduate courses, the text thoroughly covers statistical inference without delving too deep into technical details. It compares the Bayesian and frequentist schools of thought and explores procedures that lie on the border between the two. Many examples illustrate the methods and models, and exercises are included at the end of each chapter.
