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Advances in Statistical Methodologies and Their Application to Real Problems

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ISBN: 9789535131014 9789535131021 Year: Pages: 326 DOI: 10.5772/62963 Language: English
Publisher: IntechOpen
Subject: Mathematics
Added to DOAB on : 2019-10-03 07:51:50

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In recent years, statistical techniques and methods for data analysis have advanced significantly in a wide range of research areas. These developments enable researchers to analyze increasingly large datasets with more flexibility and also more accurately estimate and evaluate the phenomena they study. We recognize the value of recent advances in data analysis techniques in many different research fields. However, we also note that awareness of these different statistical and probabilistic approaches may vary, owing to differences in the datasets typical of different research fields. This book provides a cross-disciplinary forum for exploring the variety of new data analysis techniques emerging from different fields.

Bayesian Inference

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ISBN: 9789535135777 9789535135784 Year: Pages: 378 DOI: 10.5772/66264 Language: English
Publisher: IntechOpen
Subject: Statistics
Added to DOAB on : 2019-10-03 07:51:50

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The range of Bayesian inference algorithms and their different applications has been greatly expanded since the first implementation of a Kalman filter by Stanley F. Schmidt for the Apollo program. Extended Kalman filters or particle filters are just some examples of these algorithms that have been extensively applied to logistics, medical services, search and rescue operations, or automotive safety, among others. This book takes a look at both theoretical foundations of Bayesian inference and practical implementations in different fields. It is intended as an introductory guide for the application of Bayesian inference in the fields of life sciences, engineering, and economics, as well as a source document of fundamentals for intermediate Bayesian readers.

Mathematical and Statistics Anxiety: Educational, Social, Developmental and Cognitive Perspectives

Authors: --- --- --- --- et al.
Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889450763 Year: Pages: 194 DOI: 10.3389/978-2-88945-076-3 Language: English
Publisher: Frontiers Media SA
Subject: Psychology --- Science (General)
Added to DOAB on : 2017-07-06 13:27:36
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Mathematical anxiety is a feeling of tension, apprehension or fear which arises when a person is faced with mathematical content. The negative consequences of mathematical anxiety are well-documented. Students with high levels of mathematical anxiety might underperform in important test situations, they tend to hold negative attitudes towards mathematics, and they are likely to opt out of elective mathematics courses, which also affects their career opportunities. Although at the university level many students do not continue to study mathematics, social science students are confronted with the fact that their disciplines involve learning about statistics - another potential source of anxiety for students who are uncomfortable with dealing with numerical content. Research on mathematical anxiety is a truly interdisciplinary field with contributions from educational, developmental, cognitive, social and neuroscience researchers. The current collection of papers demonstrates the diversity of the field, offering both new empirical contributions and reviews of existing studies. The contributors also outline future directions for this line of research.

Just Managing?

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Book Series: Open Reports Series ISBN: 9781783743254 Year: Pages: 244 DOI: 10.11647/OBP.0112 Language: English
Publisher: Open Book Publishers
Subject: Political Science
Added to DOAB on : 2017-08-22 11:01:03
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"The 'just about managing'. 'Hardworking families'. 'Alarm-clock Britain'. In recent years British political discourse has been filled with these slogans, as politicians claim to speak on behalf of families who are in work, but struggling to get by. This book allows us to hear from some of these families directly. At a time when the impact of austerity is more relevant than ever, Just Managing? cuts through the debates and sloganeering to give some of the real people behind the headlines and statistics a chance to tell their stories. It tracks the lives of thirty working families in Liverpool over one year, as they struggle to manage on incomes at or around the National Minimum Wage. Their accounts are placed within the economic and political context that has shaped their experiences and that of millions of other working families across the country. This book is required reading for anyone seeking to understand what life is like at the sharp end of 'austerity Britain’."

Uncertainty Quantification and Model Calibration

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ISBN: 9789535132790 9789535132806 Year: Pages: 226 DOI: 10.5772/65579 Language: English
Publisher: IntechOpen
Subject: Mathematics
Added to DOAB on : 2019-10-03 07:51:50

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Uncertainty quantification may appear daunting for practitioners due to its inherent complexity but can be intriguing and rewarding for anyone with mathematical ambitions and genuine concern for modeling quality. Uncertainty quantification is what remains to be done when too much credibility has been invested in deterministic analyses and unwarranted assumptions. Model calibration describes the inverse operation targeting optimal prediction and refers to inference of best uncertain model estimates from experimental calibration data. The limited applicability of most state-of-the-art approaches to many of the large and complex calculations made today makes uncertainty quantification and model calibration major topics open for debate, with rapidly growing interest from both science and technology, addressing subtle questions such as credible predictions of climate heating.

Global wine markets, 1860 to 2016

Authors: --- ---
ISBN: 9781925261660 Year: DOI: 10.20851/global-wine-markets Language: English
Publisher: University of Adelaide Press
Subject: Economics
Added to DOAB on : 2018-01-12 11:01:57
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Until recently, most grape-based wine was consumed close to where it was produced, and mostly that was in Europe. Now more than two-fifths of all wine consumed globally is produced in another country, including in the Southern Hemisphere, the USA and Asia. This latest edition of global wine statistics not only updates data to 2016 but also adds another century of data. The motivation to assemble those historical data was to enable comparisons between the current and the previous globalization waves. This unique database reveals that, even though Europe’s vineyards were devastated by vine diseases and the pest phylloxera from the 1860s, most ‘New World’ countries remained net importers of wine until late in the nineteenth century. Some of the world’s leading wine economists and historians have contributed to and drawn on this database to examine the development of national wine market developments before, during and in between the two waves of globalization. Their initial analyses cover all key wine-producing and -consuming countries using a common methodology to explain long-term trends and cycles in national wine production, consumption, and trade.

Elements of Causal Inference

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Book Series: Adaptive Computation and Machine Learning series ISBN: 9780262344296 9780262037310 Year: Pages: 288 Language: English
Publisher: The MIT Press
Subject: Computer Science
Added to DOAB on : 2019-01-17 11:41:31
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A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

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