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Astrostatistics and Data Mining
This volume provides an overview of the field of Astrostatistics understood as the sub-discipline dedicated to the statistical analysis of astronomical data. It presents examples of the application of the various methodologies now available to current open issues in astronomical research. The techni...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer New York : Imprint: Springer,
2012.
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| Edición: | 1st ed. 2012. |
| Colección: | Springer Series in Astrostatistics,
2 |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- ??? 'Science with Gaia: how will we deal with a complex billion-source catalogue and data archive?' by Anthony Brown (Leiden University,Netherlads)
- 'Recent Advances in cosmological Bayesian model comparison' by Roberto Trotta (University College London, UK)
- 'The Art of Data Science' by Matthew Graham (Center for Advanced Computing Research, California Institute of Technology, USA)
- 'Astronomical Surveys: from SDSS to LSST' by Robert Lupton (Princeton University, USA)
- 'Exoplanet demography, quasar target selection, and probabilistic redshift estimation: Hierarchical models for density estimation, classification, and regression.' by David Hogg (New York University, USA)
- 'Learning to disentangle Exoplanet signals from correlated noise' by Suzanne Aigrain (Oxford University, UK)
- Astroinformatics and data mining: how to cope with the data tsunami' by Giuseppe Longo (Federico II University, Italy)
- Advanced statistical techniques for the processing of astronomical data: time series, images, low number statistics for high energy photons, heteroskedastic data, non-detections
- Challenges in the data mining of astronomical databases: the class imbalance in training sets or how to define prior robust preprocessing for supervised/unsupervised classification robust inference with heterogeneous datasets, how to combine observations, models, priors, etc in a training/test set error propagation
- The challenge of petabyte size databases: scalability, parallel computing, accuracy
- Geometric data organization, sky indexing for efficient data retrieval, intelligent access to petabyte size databases
- Knowledge Discovery in astronomical archives: outlier detection, new object types, parametric inference, model fitting and model selection, etc
- Combining the classical domain knowledge approach with machine learning techniques
- Global approaches for global datasets. The Galaxy zoo and the Universe zoo
- The Virtual Observatories, Data Mining and Astrostatistics: software, standards, protocols. .