Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Support Vector Machines, a robust prediction method with applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods: Theory and Practice, and more.
1. 1 Introduction - p. page_1 2. 2 Introduction to Bayesian statistical analysis - p. page_3 3. 3 Markov chain Monte Carlo background - p. page_12 4. 4 Common MCMC algorithms - p. page_16 5. 5 Markov chain Monte Carlo in practice - p. page_35 6. 6 Assessing Markov chain behavior - p. page_40 7. 7 Conclusion - p. page_63 8. Chapter 2: An information and statistical analysis pipeline for microbial metagenomic sequencing data - p. page_66 9. Chapter 3: Machine learning algorithms, applications, and practices in data science - p. page_80 10. Abstract - p. page_81 11. 1 Introduction - p. page_83 12. 2 Supervised methods - p. page_85 13. 3 Practical considerations in model building - p. page_131 14. 4 Unsupervised methods - p. page_133 15. 5 Graphical methods - p. page_151 16. 6 Deep learning - p. page_163 17. 7 Optimization - p. page_184 18. 8 Artificial intelligence - p. page_185 19. 9 Applications and laboratory exercises - p. page_197 20. Chapter 4: Bayesian model selection for high-dimensional data - p. page_206 21. Chapter 5: Competing risks: Aims and methods - p. page_248 22. Chapter 6: High-dimensional statistical inference: Theoretical development to data analytics - p. page_287 23. Abstract - p. page_289 24. 1 Introduction - p. page_289 25. 2 Mean vector testing - p. page_291 26. 3 Covariance matrix - p. page_313 27. 4 Discrete multivariate models - p. page_319 28. 5 Conclusion - p. page_330 29. Chapter 7: Big data challenges in genomics - p. page_335 30. Chapter 8: Analysis of microarray gene expression data using information theory and stochastic algorithm - p. page_348 31. Chapter 9: Human life expectancy is computed from an incomplete sets of data: Modeling and analysis - p. page_378 32. Chapter 10: Support vector machines: A robust prediction method with applications in bioinformatics - p. page_389 33. Abstract - p. page_391 34. 1 Introduction - p. page_391 35. 2 Mathematical prerequisites - p. page_393 36. 3 An introduction to support vector machines - p. page_404 37. 4 (†) An introduction to robustness and influence functions - p. page_431 38. 5 Properties of SVMs - p. page_434 39. 6 (†) Applications - p. page_452 40. Index - p. page_467
Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Support Vector Machines, a robust prediction method with applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods: Theory and Practice, and more.
1. 1 Introduction - p. page_1 2. 2 Introduction to Bayesian statistical analysis - p. page_3 3. 3 Markov chain Monte Carlo background - p. page_12 4. 4 Common MCMC algorithms - p. page_16 5. 5 Markov chain Monte Carlo in practice - p. page_35 6. 6 Assessing Markov chain behavior - p. page_40 7. 7 Conclusion - p. page_63 8. Chapter 2: An information and statistical analysis pipeline for microbial metagenomic sequencing data - p. page_66 9. Chapter 3: Machine learning algorithms, applications, and practices in data science - p. page_80 10. Abstract - p. page_81 11. 1 Introduction - p. page_83 12. 2 Supervised methods - p. page_85 13. 3 Practical considerations in model building - p. page_131 14. 4 Unsupervised methods - p. page_133 15. 5 Graphical methods - p. page_151 16. 6 Deep learning - p. page_163 17. 7 Optimization - p. page_184 18. 8 Artificial intelligence - p. page_185 19. 9 Applications and laboratory exercises - p. page_197 20. Chapter 4: Bayesian model selection for high-dimensional data - p. page_206 21. Chapter 5: Competing risks: Aims and methods - p. page_248 22. Chapter 6: High-dimensional statistical inference: Theoretical development to data analytics - p. page_287 23. Abstract - p. page_289 24. 1 Introduction - p. page_289 25. 2 Mean vector testing - p. page_291 26. 3 Covariance matrix - p. page_313 27. 4 Discrete multivariate models - p. page_319 28. 5 Conclusion - p. page_330 29. Chapter 7: Big data challenges in genomics - p. page_335 30. Chapter 8: Analysis of microarray gene expression data using information theory and stochastic algorithm - p. page_348 31. Chapter 9: Human life expectancy is computed from an incomplete sets of data: Modeling and analysis - p. page_378 32. Chapter 10: Support vector machines: A robust prediction method with applications in bioinformatics - p. page_389 33. Abstract - p. page_391 34. 1 Introduction - p. page_391 35. 2 Mathematical prerequisites - p. page_393 36. 3 An introduction to support vector machines - p. page_404 37. 4 (†) An introduction to robustness and influence functions - p. page_431 38. 5 Properties of SVMs - p. page_434 39. 6 (†) Applications - p. page_452 40. Index - p. page_467
