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| inference science meaning: Principles of Methodology Perri 6, Christine Bellamy, 2011-10-17 This book provides a comprehensive, accessible guide to social science methodology. In so doing, it establishes methodology as distinct from both methods and philosophy. Most existing textbooks deal with methods, or sound ways of collecting and analysing data to generate findings. In contrast, this innovative book shows how an understanding of methodology allows us to design research so that findings can be used to answer interesting research questions and to build and test theories. Most important things in social research (e.g., beliefs, institutions, interests, practices and social classes) cannot be observed directly. This book explains how empirical research can nevertheless be designed to make sound inferences about their nature, effects and significance. The authors examine what counts as good description, explanation and interpretation, and how they can be achieved by striking intelligent trade-offs between competing design virtues. Coverage includes: • why methodology matters; • what philosophical arguments show us about inference; • competing virtues of good research design; • purposes of theory, models and frameworks; • forming researchable concepts and typologies; • explaining and interpreting: inferring causation, meaning and significance; and • combining explanation and interpretation. The book is essential reading for new researchers faced with the practical challenge of designing research. Extensive examples and exercises are provided, based on the authors′ long experience of teaching methodology to multi-disciplinary groups. Perri 6 is Professor of Social Policy in the Graduate School in the College of Business, Law and Social Sciences at Nottingham Trent University. Chris Bellamy is Emeritus Professor of Public Administration in the Graduate School, Nottingham Trent University. |
| inference science meaning: The Charioteer Mary Renault, 1973 |
| inference science meaning: Statistical Inference as Severe Testing Deborah G. Mayo, 2018-09-20 Unlock today's statistical controversies and irreproducible results by viewing statistics as probing and controlling errors. |
| inference science meaning: The Design Inference William A. Dembski, 1998-09-13 The design inference uncovers intelligent causes by isolating their key trademark: specified events of small probability. Just about anything that happens is highly improbable, but when a highly improbable event is also specified (i.e. conforms to an independently given pattern) undirected natural causes lose their explanatory power. Design inferences can be found in a range of scientific pursuits from forensic science to research into the origins of life to the search for extraterrestrial intelligence. This challenging and provocative 1998 book shows how incomplete undirected causes are for science and breathes new life into classical design arguments. It will be read with particular interest by philosophers of science and religion, other philosophers concerned with epistemology and logic, probability and complexity theorists, and statisticians. |
| inference science meaning: Active Inference Thomas Parr, Giovanni Pezzulo, Karl J. Friston, 2022-03-29 The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding sentient behavior—a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston over years of groundbreaking research, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy. Active inference puts the action into perception. This book offers the first comprehensive treatment of active inference, covering theory, applications, and cognitive domains. Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The book emphasizes the implications of the free energy principle for understanding how the brain works. It first introduces active inference both conceptually and formally, contextualizing it within current theories of cognition. It then provides specific examples of computational models that use active inference to explain such cognitive phenomena as perception, attention, memory, and planning. |
| inference science meaning: Animal Communication Theory Ulrich Stegmann, 2013-05-02 A valuable overview and analysis of foundational concepts in animal behaviour studies, including information, meaning, communication, signals and cues. Its comprehensive introduction and numerous illustrations will make it accessible to students and researchers from a wide variety of academic backgrounds, ranging from ethology and evolutionary biology to philosophy of mind. |
| inference science meaning: Best Explanations Kevin McCain, Ted Poston, 2017-12-01 Explanatory reasoning is ubiquitous. Not only are rigorous inferences to the best explanation used pervasively in the sciences, this kind of reasoning is common in everyday life. Despite its widespread use, inference to the best explanation is still in need of precise formulation, and it remains controversial. On the one hand, supporters of explanationism take inference to the best explanation to be a justifying form of inference; some even take all justification to be a matter of explanatory reasoning. On the other hand, critics object that inference to the best explanation is not a fundamental form of inference, and some argue that we should be skeptical of inference to the best explanation in general. This volume brings together twenty philosophers to explore various aspects of inference to the best explanation and the debates surrounding it. These specially commissioned essays constitute the cutting edge of research on the role explanatory considerations play in epistemology and philosophy of science. |
| inference science meaning: Inference to the Best Explanation Peter Lipton, 2004 Inference to the Best Explanation is an unrivalled exposition of a theory of particular interest to students both of epistemology and the philosophy of science. |
| inference science meaning: Imagination and Convention Ernest LePore, Matthew Stone, 2015 How do hearers manage to understand speakers? And how do speakers manage to shape hearers' understanding? Lepore and Stone show that standard views about the workings of semantics and pragmatics are unsatisfactory. They advance an alternative view which better captures what is going on in linguistic communication. |
| inference science meaning: Data and Evidence in Linguistics András Kertész, Csilla Rákosi, 2012-02-09 The question of what types of data and evidence can be used is one of the most important topics in linguistics. This book is the first to comprehensively present the methodological problems associated with linguistic data and evidence. Its originality is twofold. First, the authors' approach accounts for a series of unexplained characteristics of linguistic theorising: the uncertainty and diversity of data, the role of evidence in the evaluation of hypotheses, the problem solving strategies as well as the emergence and resolution of inconsistencies. Second, the findings are obtained by the application of a new model of plausible argumentation which is also of relevance from a general argumentation theoretical point of view. All concepts and theses are systematically introduced and illustrated by a number of examples from different linguistic theories, and a detailed case-study section shows how the proposed model can be applied to specific linguistic problems. |
| inference science meaning: The Science of Meaning Derek Ball, Brian Rabern, 2018-07-11 By creating certain marks on paper, or by making certain sounds-breathing past a moving tongue-or by articulation of hands and bodies, language users can give expression to their mental lives. With language we command, assert, query, emote, insult, and inspire. Language has meaning. This fact can be quite mystifying, yet a science of linguistic meaning-semantics-has emerged at the intersection of a variety of disciplines: philosophy, linguistics, computer science, and psychology. |
| inference science meaning: A Companion to the Philosophy of Science W. H. Newton-Smith, 2001-10-08 Unmatched in the quality of its world-renowned contributors, this companion serves as both a course text and a reference book across the broad spectrum of issues of concern to the philosophy of science. |
| inference science meaning: Logic, Methodology and Philosophy of Science VII R. Barcan Marcus, G.J.W. Dorn, P. Weingartner, 1986-05-01 Logic, Methodology and Philosophy of Science VII |
| inference science meaning: Romantic Fantasy and Science Fiction Karl Kroeber, 1988 |
| inference science meaning: Abductive Inference Models for Diagnostic Problem-Solving Yun Peng, James A. Reggia, 2012-12-06 Making a diagnosis when something goes wrong with a natural or m- made system can be difficult. In many fields, such as medicine or electr- ics, a long training period and apprenticeship are required to become a skilled diagnostician. During this time a novice diagnostician is asked to assimilate a large amount of knowledge about the class of systems to be diagnosed. In contrast, the novice is not really taught how to reason with this knowledge in arriving at a conclusion or a diagnosis, except perhaps implicitly through ease examples. This would seem to indicate that many of the essential aspects of diagnostic reasoning are a type of intuiti- based, common sense reasoning. More precisely, diagnostic reasoning can be classified as a type of inf- ence known as abductive reasoning or abduction. Abduction is defined to be a process of generating a plausible explanation for a given set of obs- vations or facts. Although mentioned in Aristotle's work, the study of f- mal aspects of abduction did not really start until about a century ago. |
| inference science meaning: Ecological Inference Gary King, Martin A. Tanner, Ori Rosen, 2004-09-13 Drawing upon the recent explosion of research in the field, a diverse group of scholars surveys the latest strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays offers many fresh and important contributions to the study of ecological inference. |
| inference science meaning: Foundations of Inference in Natural Science J O Wisdom, 2013-04-15 Originally published in 1952. This book is a critical survey of the views of scientific inference that have been developed since the end of World War I. It contains some detailed exposition of ideas – notably of Keynes – that were cryptically put forward, often quoted, but nowhere explained. Part I discusses and illustrates the method of hypothesis. Part II concerns induction. Part III considers aspects of the theory of probability that seem to bear on the problem of induction and Part IV outlines the shape of this problem and its solution take if transformed by the present approach. |
| inference science meaning: Mathematics for Machine Learning Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, 2020-04-23 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. |
| inference science meaning: Doing Research in Political Science Paul Pennings, Hans Keman, Jan Kleinnijenhuis, 2005-11-11 This is an immensely helpful book for students starting their own research... an excellent introduction to the comparative method giving an authoritative overview over the research process - Klaus Armingeon, University of Bern Doing Research in Political Science is the book for mastering the comparative method in all the social sciences - Jan-Erik Lane, University of Geneva This book has established itself as a concise and well-readable text on comparative methods and statistics in political science I...strongly recommend it. - Dirk Berg-Schlosser, Philipps-University Marburg This thoroughly revised edition of the popular textbook offers an accessible but comprehensive introduction to comparative research methods and statistics for students of political science. Clearly organized around three parts, the text introduces the main theories and methodologies used in the discipline. Part 1 frames the comparative approach within the methodological framework of the political and social sciences. Part 2 introduces basic descriptive and inferential statistical methods as well as more advanced multivariate methods used in quantitative political analysis. Part 3 applies the methods and techniques of Parts 1 & 2 to research questions drawn from contemporary themes and issues in political science. Incorporating practice exercises, ideas for further reading and summary questions throughout, Doing Research in Political Science provides an invaluable step-by-step guide for students and researchers in political science, comparative politics and empirical political analysis. |
| inference science meaning: The Triumph of Sociobiology John Alcock, 2001-06-28 In The Triumph of Sociobiology, John Alcock reviews the controversy that has surrounded evolutionary studies of human social behavior following the 1975 publication of E.O. Wilson's classic, Sociobiology, The New Synthesis. Denounced vehemently as an ideology that has justified social evils and inequalities, sociobiology has survived the assault. Twenty-five years after the field was named by Wilson, the approach he championed has successfully demonstrated its value in the study of animal behavior, including the behavior of our own species. Yet, misconceptions remain--to our disadvantage. In this straight-forward, objective approach to the sociobiology debate, noted animal behaviorist John Alcock illuminates how sociobiologists study behavior in all species. He confronts the chief scientific and ideological objections head on, with a compelling analysis of case histories that involve such topics as sexual jealousy, beauty, gender difference, parent-offspring relations, and rape. In so doing, he shows that sociobiology provides the most satisfactory scientific analysis of social behavior available today. Alcock challenges the notion that sociobiology depends on genetic determinism while showing the shortcoming of competing approaches that rely on cultural or environmental determinism. He also presents the practical applications of sociobiology and the progress sociobiological research has made in the search for a more complete understanding of human activities. His reminder that natural behavior is not moral behavior should quiet opponents fearing misapplication of evolutionary theory to our species. The key misconceptions about this evolutionary field are dissected one by one as the author shows why sociobiologists have had so much success in explaining the puzzling and fascinating social behavior of nonhuman animals and humans alike. |
| inference science meaning: The Wretched Stone Chris Van Allsburg, 1991 A strange glowing stone picked up on a sea voyage captivates a ship's crew and has a terrible transforming effect on them. |
| inference science meaning: Handbook of Automated Reasoning Alan J.A. Robinson, Andrei Voronkov, 2001-06-21 Handbook of Automated Reasoning. |
| inference science meaning: A Solution to the Ecological Inference Problem Gary King, 2013-09-20 This book provides a solution to the ecological inference problem, which has plagued users of statistical methods for over seventy-five years: How can researchers reliably infer individual-level behavior from aggregate (ecological) data? In political science, this question arises when individual-level surveys are unavailable (for instance, local or comparative electoral politics), unreliable (racial politics), insufficient (political geography), or infeasible (political history). This ecological inference problem also confronts researchers in numerous areas of major significance in public policy, and other academic disciplines, ranging from epidemiology and marketing to sociology and quantitative history. Although many have attempted to make such cross-level inferences, scholars agree that all existing methods yield very inaccurate conclusions about the world. In this volume, Gary King lays out a unique--and reliable--solution to this venerable problem. King begins with a qualitative overview, readable even by those without a statistical background. He then unifies the apparently diverse findings in the methodological literature, so that only one aggregation problem remains to be solved. He then presents his solution, as well as empirical evaluations of the solution that include over 16,000 comparisons of his estimates from real aggregate data to the known individual-level answer. The method works in practice. King's solution to the ecological inference problem will enable empirical researchers to investigate substantive questions that have heretofore proved unanswerable, and move forward fields of inquiry in which progress has been stifled by this problem. |
| inference science meaning: Reproducibility and Replicability in Science Engineering National Academies of Sciences, National Academies of Sciences, Engineering, and Medicine (U.S.). Committee on Reproducibility and Replicability in Science, National Academies of Sciences, Engineering, and Medicine (U.S.). Nuclear and Radiation Studies Board, National Academies of Sciences, Engineering, and Medicine (U.S.). Board on Research Data and Information, National Academies of Sciences, Engineering, and Medicine (U.S.). Board on Mathematical Sciences and Analytics, 2019 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--Publisher's description |
| inference science meaning: Science Rules Peter Achinstein, 2004-09-24 Included is a famous nineteenth-century debate about scientific reasoning between the hypothetico-deductivist William Whewell and the inductivist John Stuart Mill; and an account of the realism-antirealism dispute about unobservables in science, with a consideration of Perrin's argument for the existence of molecules in the early twentieth century. |
| inference science meaning: Theory-Based Data Analysis for the Social Sciences Carol S. Aneshensel, 2013 This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of third variables to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions. |
| inference science meaning: The Epistemology of Statistical Science Mauritz Van Aarde, 2009-12-01 Whilst this is a book about higher education, there are important lessons for schooling. On the one hand, the book is a powerful demonstration of the potential of DST for enhancing learning in schools, particularly in schools serving the poor and marginalised. On the other hand, improving teaching and learning in higher education, through the creative use of technology, is essential to overcome the learning challenges of those entering tertiary level institutions. |
| inference science meaning: Arguing About Science Alexander Bird, James Ladyman, 2012-11-12 Arguing About Science is an outstanding, engaging introduction to the essential topics in philosophy of science, edited by two leading experts in the field. This exciting and innovative anthology contains a selection of classic and contemporary readings that examine a broad range of issues, from classic problems such as scientific reasoning; causation; and scientific realism, to more recent topics such as science and race; forensic science; and the scientific status of medicine. The editors bring together some of the most influential contributions of famous philosophers in the field, including John Stuart Mill and Karl Popper, as well as more recent extracts from philosophers and scientists such as Ian Hacking, Stephen Jay Gould, Bas van Fraassen, Nancy Cartwright, and John Worrall. The anthology is organised into nine clear sections: science, non science and pseudo-science race, gender and science scientific reasoning scientific explanation laws and causation science and medicine probability and forensic science risk, uncertainty and science policy scientific realism and anti-realism. The articles chosen are clear, interesting, and free from unnecessary jargon. The editors provide lucid introductions to each section in which they provide an overview of the debate, as well as suggestions for further reading. |
| inference science meaning: The Encyclopaedia Britannica , 1911 |
| inference science meaning: Large-Scale Inference Bradley Efron, 2012-11-29 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. |
| inference science meaning: The Encyclopaedia Britannica Hugh Chrisholm, 1911 |
| inference science meaning: Listening and Note-taking Virginia Yates, 1979 |
| inference science meaning: Bayesian Data Analysis, Third Edition Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 2013-11-01 Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page. |
| inference science meaning: Science John Michels (Journalist), 1926 |
| inference science meaning: Causal Inference in Statistics Judea Pearl, Madelyn Glymour, Nicholas P. Jewell, 2016-01-25 CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as Does this treatment harm or help patients? But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding. |
| inference science meaning: Routledge Handbook of Interpretive Political Science Mark Bevir, R. A. W. Rhodes, 2015-07-03 Interpretive political science focuses on the meanings that shape actions and institutions, and the ways in which they do so. This Handbook explores the implications of interpretive theory for the study of politics. It provides the first definitive survey of the field edited by two of its pioneers. Written by leading scholars from a range of disciplinary backgrounds, the Handbook’s 32 chapters are split into five parts which explore: the contrast between interpretive theory and mainstream political science; the main forms of interpretive theory and the theoretical concepts associated with interpretive political science; the methods used by interpretive political scientists; the insights provided by interpretive political science on empirical topics; the implications of interpretive political science for professional practices such as policy analysis, planning, accountancy, and public health. With an emphasis on the applications of interpretive political science to a range of topics and disciplines, this Handbook is an invaluable resource for students, scholars, and practitioners in the areas of international relations, comparative politics, political sociology, political psychology, and public administration. |
| inference science meaning: Causal Inference in Statistics, Social, and Biomedical Sciences Guido W. Imbens, Donald B. Rubin, 2015-04-06 This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments. |
| inference science meaning: The Principles of Science Jevons, 1879 |
| inference science meaning: The Principles of Science William Stanley Jevons, 1879 |
| inference science meaning: The Principles of Science. A Treatise on Logic and Scientific Method William Stanley Jevons, 2024-06-20 Reprint of the original, first published in 1877. |
机器学习中Inference 和predict的区别是什么? - 知乎
Inference: You want to understand how ozone levels are influenced by temperature, solar radiation, and wind. Since you assume that the residuals are normally distributed, you use a linear …
如何简单易懂地理解变分推断 (variational inference)？ - 知乎
How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large …
因果推断会是下一个AI热潮吗？ - 知乎
The Causal-Neural Connection: Expressiveness, Learnability, and Inference Kevin M Xia (Columbia University) · Kai-Zhan Lee (Columbia University) · Yoshua Bengio (University of Montreal) · Elias …
统计学方面国内外有哪些权威的期刊？ - 知乎
国内暂时没有看到专门的统计的权威期刊，学科评审只列了经济研究 外文期刊中的四大天王 jrssb aos jasa biometrika 以及和四大天王并驾齐驱的 joe 这上面的五大期刊应该是统计学最为权威的期刊 更新 …
面板数据用聚类稳健标准误，结果变得不显著；而不用的情况下很 …
据我所知，英文顶刊对标准误的聚类要求是很严的，至少要聚类到核心解释变量层面，有的还会往更高层面聚类作为稳健性检验。国内较少对标准误的聚类做要求，部分中文顶刊文章都不说明采用什么标准 …
英文文献如何正确导出为参考文献格式？ - 知乎
英文专业的小伙伴们，还在为论文的参考文献格式苦恼和纠结吗？不必担心，小编都整理好了，实实在在的干货！ 英文参考文献格式一般来说有APA（美国心理学会American Psychological Association） …
双向固定效应模型怎么理解？ - 知乎
那么，培训时长能够解释这一差异吗？ 这就是二维固定效应模型的意义。 参考资料： Scott Cunningham，Causal Inference The Mixtape Causal Inference for The Brave and True The Effect: …
Tesla P100和GTX 1080性能对比怎样？ - 知乎
训练神经网络， 1080ti 和P100没有太大差别。 有人说inference差很多，我不太相信但是也没做过实验。 总之你自费买卡做实验的话，没有任何理由买P100.
机器学习中Inference 和predict的区别是什么? - 知乎
Inference: You want to understand how ozone levels are influenced by temperature, solar radiation, and wind. Since you assume that the residuals are normally distributed, you use a …
如何简单易懂地理解变分推断 (variational inference)？ - 知乎
How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large …
因果推断会是下一个AI热潮吗？ - 知乎
The Causal-Neural Connection: Expressiveness, Learnability, and Inference Kevin M Xia (Columbia University) · Kai-Zhan Lee (Columbia University) · Yoshua Bengio (University of …
统计学方面国内外有哪些权威的期刊？ - 知乎
国内暂时没有看到专门的统计的权威期刊，学科评审只列了经济研究 外文期刊中的四大天王 jrssb aos jasa biometrika 以及和四大天王并驾齐驱的 joe 这上面的五大期刊应该是统计学最为权威的 …
面板数据用聚类稳健标准误，结果变得不显著；而不用的情况下很 …
据我所知，英文顶刊对标准误的聚类要求是很严的，至少要聚类到核心解释变量层面，有的还会往更高层面聚类作为稳健性检验。国内较少对标准误的聚类做要求，部分中文顶刊文章都不说明 …
英文文献如何正确导出为参考文献格式？ - 知乎
英文专业的小伙伴们，还在为论文的参考文献格式苦恼和纠结吗？不必担心，小编都整理好了，实实在在的干货！ 英文参考文献格式一般来说有APA（美国心理学会American Psychological …
双向固定效应模型怎么理解？ - 知乎
那么，培训时长能够解释这一差异吗？ 这就是二维固定效应模型的意义。 参考资料： Scott Cunningham，Causal Inference The Mixtape Causal Inference for The Brave and True The …
Tesla P100和GTX 1080性能对比怎样？ - 知乎
训练神经网络， 1080ti 和P100没有太大差别。 有人说inference差很多，我不太相信但是也没做过实验。 总之你自费买卡做实验的话，没有任何理由买P100.
