Lectures on Gaussian Processes

  • Mikhail Lifshits

Part of the SpringerBriefs in Mathematics book series (BRIEFSMATH)

Table of contents

  1. Front Matter
    Pages i-x
  2. Mikhail Lifshits
    Pages 1-117
  3. Back Matter
    Pages 119-121

About this book


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. In university teaching, one can build a one-semester advanced course upon these Briefs.


60G15 Gaussian processes, Gaussian measures Reproducing Kernel Hilbert Space (RKHS) isoperimetric inequalities large deviations small deviations

Authors and affiliations

  • Mikhail Lifshits
    • 1
  1. 1.Department of Mathematics, and MechanicsSt. Petersburg State UniversityStary PeterhofRussia

Bibliographic information

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