Statistical data science:
2. Probabilistic Numerical Methods2.1. What is probabilistic numerics?; 2.2. The future of probabilistic numerics; 3. Conclusion; Acknowledgements; References; 7. Phylogenetic Gaussian Processes for Bat Echolocation; 1. Introduction; 2. Echolocation Calls as Function-Valued Traits; 3. Phylogenetic G...
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| Weitere Verfasser: | , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New Jersey ; London ; Singapore ; Beijing ; Shanghai ; Hong Kong ; Taipei ; Chennai ; Tokyo
World Scientific
[2018]
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| Schlagworte: | |
| Online-Zugang: | FWS01 FWS02 TUM01 UBY01 |
| Zusammenfassung: | 2. Probabilistic Numerical Methods2.1. What is probabilistic numerics?; 2.2. The future of probabilistic numerics; 3. Conclusion; Acknowledgements; References; 7. Phylogenetic Gaussian Processes for Bat Echolocation; 1. Introduction; 2. Echolocation Calls as Function-Valued Traits; 3. Phylogenetic Gaussian Processes; 4. Results; 4.1. Data description; 4.2. Hyperparameter estimation and ancestral trait reconstruction with phylogenetic Gaussian processes; 5. Conclusions and Further Work; Acknowledgement; References 3.2. L-kernel density estimation3.2.1. Choice of L-kernel; 3.2.2. Relationship to the existing literature; 4. Marginal Likelihood Estimation; 4.1. L-kernel estimation of marginal likelihood; 4.2. Mixture modelling; 4.2.1. Likelihood; 4.2.2. Prior distribution; 4.2.3. Label switching; 4.3. Inference; 4.4. Example: Galaxy data; 5. Discussion; Acknowledgements; References; 6. Bayesian Numerical Methods as a Case Study for Statistical Data Science; 1. The Rise of Data Science; 1.1. Data science: Does it matter, or is it all hype?; 1.2. Nightmares and opportunities for statisticians 3. Evaluating Statistical and Machine Learning Supervised Classification Methods1. Introduction; 2. Performance Measures; 2.1. Measures based on the classification table; 2.2. Choosing the classification threshold; 2.2.1. Misclassification rate; 2.2.2. Kolmogorov-Smirnov measure; 2.2.3. Given misclassification costs; 2.2.4. The F-measure; 2.3. Distributions of misclassification costs; 2.3.1. The H-measure; 2.3.2. Screening; 3. Conclusion; References; 4. Diversity as a Response to User Preference Uncertainty; 1. Introduction; 2. Problem Formulation; 3. Solution Method; 4. Simulation Study |
| Beschreibung: | 1 Online-Ressource |
| ISBN: | 1786345404 9781786345400 9781786345417 |
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| 520 | 3 | |a 2. Probabilistic Numerical Methods2.1. What is probabilistic numerics?; 2.2. The future of probabilistic numerics; 3. Conclusion; Acknowledgements; References; 7. Phylogenetic Gaussian Processes for Bat Echolocation; 1. Introduction; 2. Echolocation Calls as Function-Valued Traits; 3. Phylogenetic Gaussian Processes; 4. Results; 4.1. Data description; 4.2. Hyperparameter estimation and ancestral trait reconstruction with phylogenetic Gaussian processes; 5. Conclusions and Further Work; Acknowledgement; References | |
| 520 | 3 | |a 3.2. L-kernel density estimation3.2.1. Choice of L-kernel; 3.2.2. Relationship to the existing literature; 4. Marginal Likelihood Estimation; 4.1. L-kernel estimation of marginal likelihood; 4.2. Mixture modelling; 4.2.1. Likelihood; 4.2.2. Prior distribution; 4.2.3. Label switching; 4.3. Inference; 4.4. Example: Galaxy data; 5. Discussion; Acknowledgements; References; 6. Bayesian Numerical Methods as a Case Study for Statistical Data Science; 1. The Rise of Data Science; 1.1. Data science: Does it matter, or is it all hype?; 1.2. Nightmares and opportunities for statisticians | |
| 520 | 3 | |a 3. Evaluating Statistical and Machine Learning Supervised Classification Methods1. Introduction; 2. Performance Measures; 2.1. Measures based on the classification table; 2.2. Choosing the classification threshold; 2.2.1. Misclassification rate; 2.2.2. Kolmogorov-Smirnov measure; 2.2.3. Given misclassification costs; 2.2.4. The F-measure; 2.3. Distributions of misclassification costs; 2.3.1. The H-measure; 2.3.2. Screening; 3. Conclusion; References; 4. Diversity as a Response to User Preference Uncertainty; 1. Introduction; 2. Problem Formulation; 3. Solution Method; 4. Simulation Study | |
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| id | DE-604.BV045871237 |
| illustrated | Not Illustrated |
| indexdate | 2024-08-01T15:15:17Z |
| institution | BVB |
| isbn | 1786345404 9781786345400 9781786345417 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031254569 |
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| publisher | World Scientific |
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| spellingShingle | Statistical data science Statistik (DE-588)4056995-0 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4428654-5 (DE-588)1071861417 |
| title | Statistical data science |
| title_auth | Statistical data science |
| title_exact_search | Statistical data science |
| title_full | Statistical data science Editors Niall Adams, Ed Cohen, Imperial College London, UK |
| title_fullStr | Statistical data science Editors Niall Adams, Ed Cohen, Imperial College London, UK |
| title_full_unstemmed | Statistical data science Editors Niall Adams, Ed Cohen, Imperial College London, UK |
| title_short | Statistical data science |
| title_sort | statistical data science |
| topic | Statistik (DE-588)4056995-0 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Statistik Data Mining Konferenzschrift 2017 London |
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