Bayesian Machine Learning in Quantitative Finance, 9783031884306
Hardcover
Bayesian machine learning empowers smarter financial modeling and informed decision-making.
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Bayesian Machine Learning in Quantitative Finance

theory and practical applications

$287.49

  • Hardcover

    329 pages

  • Release Date

    20 July 2025

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Summary

Bayesian Machine Learning: A Quantitative Finance Perspective

This book offers a comprehensive discussion of the Bayesian inference framework and demonstrates why this probabilistic approach is ideal for tackling the various modelling problems within quantitative finance. It demonstrates how advanced Bayesian machine learning techniques can be applied within financial engineering, investment portfolio management, insurance, municipal finance management as well as banking.

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Book Details

ISBN-13:9783031884306
ISBN-10:3031884302
Author:Wilson Tsakane Mongwe, Rendani Mbuvha, Tshilidzi Marwala
Publisher:Springer International Publishing AG
Imprint:Palgrave Macmillan
Format:Hardcover
Number of Pages:329
Release Date:20 July 2025
Weight:0g
Dimensions:210mm x 148mm
About The Author

Wilson Tsakane Mongwe

Wilson Tsakane Mongwe is a machine learning research fellow at the University of Johannesburg, South Africa, and an Associate Director and the Head Quantitative Analyst at a Big Four audit firm’s Financial Services Advisory business unit. He was previously the Head of Market Risk and Capital Analytics at a tier 1 South African bank’s Global Markets division. He holds a Masters in Mathematical Finance from the University of Cape Town and a PhD in Artificial Intelligence from the University of Johannesburg. He was awarded a Google PhD fellowship in machine learning in 2020, which supported his PhD research. He received the Mail and Guardian Top 200 Young South Africans award for 2020, and was recognized as one of 30 Young Mandela’s in 2022 by News24. He is an author of the machine learning book entitled “Hamiltonian Monte Carlo Methods in Machine Learning”.

Rendani Mbuvha is currently Associate Professor of Actuarial Science in the Department of Mathematics at the University of Manchester, UK. Previously, he served as a Google DeepMind Academic Fellow with Queen Mary University of London (QMUL) and as an Associate Professor at the University of Witwatersrand, South Africa. He is a fellow of the Institute and Faculty of Actuaries and Co-Founder of AfriClimate AI, a grassroots community focused on AI for Climate action research in the global south. He has published extensively in probabilistic inference in machine learning, renewable energy modeling, and computational finance.

Tshilidzi Marwala is United Nations Under-Secretary-General and Rector of the UN University and was formerly the Vice-Chancellor of the University of Johannesburg (UJ), South Africa. He was the trustee of the Nelson Mandela Foundation and is a member of the American Academy of Arts and Sciences, Chinese Academy of Sciences, The World Academy of Sciences and the African Academy of Sciences. He holds a Bachelor of Science in Mechanical Engineering from Case Western Reserve University and a PhD in Artificial Intelligence from the University of Cambridge. He has published 27 books in artificial intelligence and related areas.

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