Book contents
- Data and Methods in Corpus Linguistics
- Data and Methods in Corpus Linguistics
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Acknowledgements
- Introduction: Comparative Approaches to Data and Methods in Corpus Linguistics
- Part I Corpus Dimensions and the Viability of Methodological Approaches
- Part II Selection, Calibration and Preparation of Corpus Data
- Part III Perspectives on Multifactorial Methods
- 6 Comparing Generalised Linear Mixed-Effects Models, Generalised Linear Mixed-Effects Model Trees and Random Forests
- 7 Comparing Logistic Regression, Multinomial Regression, Classification Trees and Random Forests Applied to Ternary Variables
- 8 Comparing Bayesian and Frequentist Models of Language Variation
- 8.1 Aims
- 8.2 Frequentist and Bayesian Inference in Contrast
- 8.3 Previous Research on Help
- 8.4 Data and Variables
- 8.5 Models Based on the Large Dataset
- 8.6 Models Based on the Small Sample
- 8.7 Summary and Reading Suggestions
- Appendix 8.1 Table of Coefficients for the ML Model Based on the Large Sample, and Some Other Details
- Appendix 8.2 Table of Coefficients for the ML Model Based on the Large Sample, without the Helper (Parsimonious Model), and Some Other Details
- Appendix 8.3 Bayesian Model Based on the Large Sample with Weakly Informative Cauchy Priors
- Appendix 8.4 An ML Model Based on the Small Sample
- Appendix 8.5 A Bayesian Model with Informative Priors Based on the Small Sample
- Appendix 8.6 Different Priors and Sensitivity Analysis
- Appendix 8.7 Diagnostics of Markov Chains
- 9 Comparing Methods for the Evaluation of Cluster Structures in Multidimensional Analyses
- Part IV Applications of Classification-Based Approaches
- Index
Appendix 8.7 - Diagnostics of Markov Chains
from 8 - Comparing Bayesian and Frequentist Models of Language Variation
Published online by Cambridge University Press: 06 May 2022
Book contents
- Data and Methods in Corpus Linguistics
- Data and Methods in Corpus Linguistics
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Acknowledgements
- Introduction: Comparative Approaches to Data and Methods in Corpus Linguistics
- Part I Corpus Dimensions and the Viability of Methodological Approaches
- Part II Selection, Calibration and Preparation of Corpus Data
- Part III Perspectives on Multifactorial Methods
- 6 Comparing Generalised Linear Mixed-Effects Models, Generalised Linear Mixed-Effects Model Trees and Random Forests
- 7 Comparing Logistic Regression, Multinomial Regression, Classification Trees and Random Forests Applied to Ternary Variables
- 8 Comparing Bayesian and Frequentist Models of Language Variation
- 8.1 Aims
- 8.2 Frequentist and Bayesian Inference in Contrast
- 8.3 Previous Research on Help
- 8.4 Data and Variables
- 8.5 Models Based on the Large Dataset
- 8.6 Models Based on the Small Sample
- 8.7 Summary and Reading Suggestions
- Appendix 8.1 Table of Coefficients for the ML Model Based on the Large Sample, and Some Other Details
- Appendix 8.2 Table of Coefficients for the ML Model Based on the Large Sample, without the Helper (Parsimonious Model), and Some Other Details
- Appendix 8.3 Bayesian Model Based on the Large Sample with Weakly Informative Cauchy Priors
- Appendix 8.4 An ML Model Based on the Small Sample
- Appendix 8.5 A Bayesian Model with Informative Priors Based on the Small Sample
- Appendix 8.6 Different Priors and Sensitivity Analysis
- Appendix 8.7 Diagnostics of Markov Chains
- 9 Comparing Methods for the Evaluation of Cluster Structures in Multidimensional Analyses
- Part IV Applications of Classification-Based Approaches
- Index
- Type
- Chapter
- Information
- Data and Methods in Corpus LinguisticsComparative Approaches, pp. 257 - 258Publisher: Cambridge University PressPrint publication year: 2022