Complex data modeling and computationally intensive statistical methods
The last years have seen the advent and development of many devices able to record and store an always increasing amount of complex and high dimensional data; 3D images generated by medical scanners or satellite remote sensing, DNA microarrays, real time financial data, system control datasets, .......
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
Milano :
Springer Milan
2010.
Cham : Springer Nature |
| Collection: | Contributions to Statistics
|
| Accès en ligne: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Note: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Complex Data Modeling and Computationally Intensive Statistical Methods, Texte imprimé, 9788847013858 • Complex Data Modeling and Computationally Intensive Statistical Methods, Texte imprimé, 9788847013926 • Complex Data Modeling and Computationally Intensive Statistical Methods, Texte imprimé, 9788847058064 |
Table des matières:
- Space-time texture analysis in thermal infrared imaging for classification of Raynaud s Phenomenon
- Mixed-effects modelling of Kevlar fibre failure times through Bayesian non-parametrics
- Space filling and locally optimal designs for Gaussian Universal Kriging
- Exploitation, integration and statistical analysis of the Public Health Database and STEMI Archive in the Lombardia region
- Bootstrap algorithms for variance estimation in ?PS sampling
- Fast Bayesian functional data analysis of basal body temperature
- A parametric Markov chain to model age- and state-dependent wear processes
- Case studies in Bayesian computation using INLA
- A graphical models approach for comparing gene sets
- Predictive densities and prediction limits based on predictive likelihoods
- Computer-intensive conditional inference
- Monte Carlo simulation methods for reliability estimation and failure prognostics.