£43.02

Springer Gaussian Process Models for Quantitative Finance Book

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Description

Expand your knowledge of mathematical finance with Gaussian Process (GP) models. This text from Springer explores how machine learning frameworks can be integrated into the field of quantitative finance. It serves as a comprehensive resource for graduate students, established researchers, and quantitative practitioners looking to master GP methodology. The book is structured into two distinct sections to support different learning stages. The first half provides a systematic and rigorous introduction to GP fundamentals. This section builds a strong foundation for those new to the methodology. The second half moves into advanced techniques, covering essential topics such as kernel choice, shape-constrained GPs, and GP gradients. Whether you are studying the basics or looking for specialized applications, this book provides the technical depth required for modern financial modeling.

Key Features

Covers diverse applications of Gaussian Process models specifically within the field of mathematical finance.

Provides a rigorous introduction to GP fundamentals suitable for graduate students and new researchers.

Explores advanced technical topics including kernel choice and GP gradients for professional practitioners.

Integrates modern machine learning frameworks into traditional quantitative finance methodologies.

Includes specialized instruction on shape-constrained GPs to enhance modeling accuracy.

B08GH5QQ9Z

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
07 March 2025
Listed Since
09 November 2024

Barcode

No barcode data available

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