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Springer Gaussian Process Models for Quantitative Finance - SpringerBriefs

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Description

This SpringerBriefs volume explores the practical applications of Gaussian Process models within mathematical finance, bridging machine learning and quantitative methods. Designed for graduate students, researchers, and quant practitioners, the book provides a systematic introduction to GP fundamentals and advanced techniques including kernel selection, shape-constrained models, and GP gradients. The first half establishes foundational knowledge and methodology, whilst the second portion deepens expertise through rigorous exploration of specialised topics. Whether you are developing trading strategies, risk management systems, or predictive financial models, this text equips you with the theoretical framework and practical insights needed to implement Gaussian Process techniques effectively in professional finance roles.

Key Features

Gaussian Process Models for Quantitative Finance by Springer integrates machine learning frameworks directly into quantitative finance applications and methodologies.

SpringerBriefs format delivers concentrated expertise on GP fundamentals, kernel choice, shape-constrained GPs, and gradient techniques for finance professionals.

The book serves graduate students, established researchers, and quant practitioners seeking rigorous introduction and advanced GP methodology for mathematical finance.

Systematic coverage of both foundational concepts and sophisticated techniques enables practitioners to build and deploy Gaussian Process models in real-world finance.

Product Specifications

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

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