Lectures on Gaussian Processes
Gaussian processes can be viewed as a far-reaching infinite-dimensional extension of classical normal random variables. Their theory presents a powerful range of tools for probabilistic modelling in various academic and technical domains such as Statistics, Forecasting, Finance, Information Transmi...
Uloženo v:
| Hlavní autor: | |
|---|---|
| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Vydání: | 2012. |
| Edice: | SpringerBriefs in Mathematics
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| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Poznámka: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Lectures on Gaussian Processes, Texte imprimé, 9783642249402 • Lectures on Gaussian Processes, Mikhail Lifshits, Heidelberg, Springer, 2012, 1 vol. (X-121 p.), SpringerBriefs in Mathematics, 978-3-642-24938-9 |
Obsah:
- Preface 1.Gaussian Vectors and Distributions 2.Examples of Gaussian Vectors, Processes and Distributions 3.Gaussian White Noise and Integral Representations 4.Measurable Functionals and the Kernel 5.Cameron-Martin Theorem 6.Isoperimetric Inequality 7.Measure Concavity and Other Inequalities 8.Large Deviation Principle 9.Functional Law of the Iterated Logarithm 10.Metric Entropy and Sample Path Properties 11.Small Deviations 12.Expansions of Gaussian Vectors 13.Quantization of Gaussian Vectors 14.Invitation to Further Reading References