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1. |
Record Nr. |
UNINA9910827645903321 |
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Autore |
Gavin Michael |
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Titolo |
Literary mathematics : quantitative theory for textual studies / / Michael Gavin |
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Pubbl/distr/stampa |
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Stanford, California : , : Stanford University Press, , [2023] |
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©2023 |
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ISBN |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (282 pages) |
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Collana |
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Stanford text technologies |
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Disciplina |
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Soggetti |
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Digital humanities |
Quantitative research |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Frontmatter -- CONTENTS -- ACKNOWLEDGMENTS -- INTRODUCTION: THE CORPUS AS AN OBJECT OF STUDY -- CHAPTER 1. NETWORKS AND THE STUDY OF BIBLIOGRAPHICAL METADATA -- CHAPTER 2. THE COMPUTATION OF MEANING -- CHAPTER 3. CONCEPTUAL TOPOGRAPHY -- CHAPTER 4. PRINCIPLES OF LITERARY MATHEMATICS -- CONCLUSION: SIMILAR WORDS TEND TO APPEAR IN DOCUMENTS WITH SIMILAR METADATA -- NOTES -- BIBLIOGRAPHY -- INDEX |
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Sommario/riassunto |
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Across the humanities and social sciences, scholars increasingly use quantitative methods to study textual data. Considered together, this research represents an extraordinary event in the long history of textuality. More or less all at once, the corpus has emerged as a major genre of cultural and scientific knowledge. In Literary Mathematics, Michael Gavin grapples with this development, describing how quantitative methods for the study of textual data offer powerful tools for historical inquiry and sometimes unexpected perspectives on theoretical issues of concern to literary studies. Student-friendly and accessible, the book advances this argument through case studies drawn from the Early English Books Online corpus. Gavin shows how a copublication network of printers and authors reveals an uncannily accurate picture of historical periodization; that a vector-space semantic model parses historical concepts in incredibly fine detail; and that a geospatial analysis of early modern discourse offers a surprising |
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panoramic glimpse into the period's notion of world geography. Across these case studies, Gavin challenges readers to consider why corpus-based methods work so effectively and asks whether the successes of formal modeling ought to inspire humanists to reconsider fundamental theoretical assumptions about textuality and meaning. As Gavin reveals, by embracing the expressive power of mathematics, scholars can add new dimensions to digital humanities research and find new connections with the social sciences. |
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2. |
Record Nr. |
UNINA9910874655003321 |
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Autore |
Einbeck Jochen |
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Titolo |
Developments in Statistical Modelling / / edited by Jochen Einbeck, Hyeyoung Maeng, Emmanuel Ogundimu, Konstantinos Perrakis |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (281 pages) |
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Collana |
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Contributions to Statistics, , 2628-8966 |
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Altri autori (Persone) |
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MaengHyeyoung |
OgundimuEmmanuel |
PerrakisKonstantinos |
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Disciplina |
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Soggetti |
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Statistics |
Regression analysis |
Machine learning |
Biometry |
Statistical Theory and Methods |
Linear Models and Regression |
Bayesian Inference |
Statistical Learning |
Biostatistics |
Anàlisi de xarxes (Planificació) |
Estadística matemàtica |
Processos gaussians |
Llibres electrònics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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REML for two dimensional P splines -- Learning Bayesian networks from ordinal data The Bayesian way -- Latent Dirichlet allocation and hidden Markov models to identify public perception of sustainability in social media data -- Bayesian approaches to model overdispersion in Spatio temporal binomial data -- Elicitation of priors for intervention effects in educational trial data -- Elicitation of priors for intervention effects in educational trial data -- Optimism correction of the AUC with complex survey data -- Statistical models for patient centered outcomes in clinical studies -- Bayesian hidden Markov models for early warning -- A Bayesian Markov-switching for smooth modelling of extreme value distributions. |
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Sommario/riassunto |
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This volume on the latest developments in statistical modelling is a collection of refereed papers presented at the 38th International Workshop on Statistical Modelling, IWSM 2024, held from 14 to 19 July 2024 in Durham, UK. The contributions cover a wide range of topics in statistical modelling, including generalized linear models, mixture models, regularization techniques, hidden Markov models, smoothing methods, censoring and imputation techniques, Gaussian processes, spatial statistics, shape modelling, goodness-of-fit problems, and network analysis. Various highly topical applications are presented as well, especially from biostatistics. The approaches are equally frequentist and Bayesian, a categorization the statistical modelling community has synergetically overcome. The book also features the workshop’s keynote contribution on statistical modelling for big and little data, highlighting that both small and large data sets come with their own challenges. The International Workshop on Statistical Modelling (IWSM) is the annual workshop of the Statistical Modelling Society, with the purpose of promoting important developments, extensions, and applications in statistical modelling, and bringing together statisticians working on related problems from various disciplines. This volume reflects this spirit and contributes to initiating and sustaining discussions about problems in statistical modelling and triggers new developments and ideas in the field. |
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