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 Trans...
保存先:
| 第一著者: | |
|---|---|
| フォーマット: | 図書 |
| 言語: | Undetermined |
| 出版事項: |
New York
Springer
2012
|
| 主題: | |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|
| Thư viện lưu trữ: | Trung tâm Học liệu Trường Đại học Cần Thơ |
|---|
| 要約: | 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 Transmission, Machine Learning - to mention just a few. The objective of these Briefs is to present a quick and condensed treatment of the core theory that a reader must understand in order to make his own independent contributions. The primary intended readership are PhD/Masters students and researchers working in pure or applied mathematics. The first chapters introduce essentials of the classical theory of Gaussian processes and measures with the core notions of reproducing kernel, integral representation, isoperimetric property, large deviation principle. The brevity being a priority for teaching and learning purposes, certain technical details and proofs are omitted. The later chapters touch important recent issues not sufficiently reflected in the literature, such as small deviations, expansions, and quantization of processes. |
|---|