國立高雄大學圖資館 |

Language: English

Back

Bayesian machine learning in quantitative financetheory and practical applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian machine learning in quantitative financeby Wilson Tsakane Mongwe, Rendani Mbuvha, Tshilidzi Marwala.
Reminder of title:	theory and practical applications /
Author:	Mongwe, Wilson Tsakane.
other author:	Mbuvha, Rendani.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xxxix, 329 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Bayesian statistical decision theory.
Online resource:	https://doi.org/10.1007/978-3-031-88431-3
ISBN:	9783031884313$q(electronic bk.)

Bayesian machine learning in quantitative financetheory and practical applications /
Mongwe, Wilson Tsakane.

Bayesian machine learning in quantitative financetheory and practical applications /[electronic resource] :by Wilson Tsakane Mongwe, Rendani Mbuvha, Tshilidzi Marwala. - Cham :Springer Nature Switzerland :2025. - xxxix, 329 p. :ill. (chiefly color), digital ;24 cm.

1 Introduction To Bayesian Machine Learning In Quantitative Finance -- 2 Background To Bayesian Machine Learning In Quantitative Finance -- 3 On the Stochastic Alpha Beta Rho Model and Hamiltonian Monte Carlo Techniques -- 4 Learning Equity Volatility Surfaces using Sparse Gaussian Processes -- 5 Analyzing South African Equity Option Prices Using Normalizing Flows -- 6 Sparse and Distributed Gaussian Processes For Modeling Corporate Credit Ratings -- 7 Bayesian Detection Of Recovery On Charged-Off Loan Accounts -- 8 Bayesian Audit Outcome Model Selection Using Normalising Flows -- 9 Bayesian Detection Of Unauthorized Expenditure Using Langevin and Hamiltonian Monte Carlo -- 10 Bayesian Neural Network Inference Of Motor Insurance Claims -- 11 Shadow and Adaptive Hamiltonian Monte Carlo Methods For Calibrating The Nelson and Siegel Model -- 12 Static and Dynamic Nested Sampling For Yield Curve Model Selection -- 13 A Bayesian Investment Analyst On The Johannesburg Stock Exchange -- 14 Conclusions to Bayesian Machine Learning In Quantitative Finance.

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. The book covers a broad range of modelling approaches, including Bayesian neural networks, Gaussian processes and Markov Chain Monte Carlo methods. It also discusses the utility of Bayesian inference in quantitative finance and discusses future research goals in the applications of Bayesian machine learning in quantitative finance. Chapters are rooted in the theory of quantitative finance and machine learning while also outlining a range of practical considerations for implementing Bayesian techniques into real-world quantitative finance problems. This book is ideal for graduate researchers and practitioners at the intersection of machine learning and quantitative finance, as well as those working in computational statistics and computer science more broadly. 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 is an author of the machine learning book entitled "Hamiltonian Monte Carlo Methods in Machine Learning". Rendani Mbuvha is Associate Professor of Actuarial Science in the Department of Mathematics at the University of Manchester, UK. He is a fellow of the Institute and Faculty of Actuaries and Co-Founder of AfriClimate AI. 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. 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 has published 27 books in artificial intelligence and related areas.

ISBN: 9783031884313$q(electronic bk.)

Standard No.: 10.1007/978-3-031-88431-3doiSubjects--Topical Terms:

182005
Bayesian statistical decision theory.

LC Class. No.: HG176.5

Dewey Class. No.: 332.01519542

Bayesian machine learning in quantitative financetheory and practical applications /
LDR:04438nmm a2200337 a 4500 001 684976
003 DE-He213
005 20250622130216.0
006 m d
007 cr nn 008maaau
008 260129s2025 sz s 0 eng d
020 $a 9783031884313$q(electronic bk.)
020 $a 9783031884306$q(paper)
024 7 $a 10.1007/978-3-031-88431-3 $2 doi
035 $a 978-3-031-88431-3
040 $a GP $c GP
041 0 $a eng
050 4 $a HG176.5
072 7 $a KFFH $2 bicssc
072 7 $a BUS017000 $2 bisacsh
072 7 $a KFFH $2 thema
082 0 4 $a 332.01519542 $2 23
090 $a HG176.5 $b .M743 2025
100 1 $a Mongwe, Wilson Tsakane. $3 998605
245 1 0 $a Bayesian machine learning in quantitative finance $h [electronic resource] : $b theory and practical applications / $c by Wilson Tsakane Mongwe, Rendani Mbuvha, Tshilidzi Marwala.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Palgrave Macmillan, $c 2025.
300 $a xxxix, 329 p. : $b ill. (chiefly color), digital ; $c 24 cm.
338 $a online resource $b cr $2 rdacarrier
505 0 $a 1 Introduction To Bayesian Machine Learning In Quantitative Finance -- 2 Background To Bayesian Machine Learning In Quantitative Finance -- 3 On the Stochastic Alpha Beta Rho Model and Hamiltonian Monte Carlo Techniques -- 4 Learning Equity Volatility Surfaces using Sparse Gaussian Processes -- 5 Analyzing South African Equity Option Prices Using Normalizing Flows -- 6 Sparse and Distributed Gaussian Processes For Modeling Corporate Credit Ratings -- 7 Bayesian Detection Of Recovery On Charged-Off Loan Accounts -- 8 Bayesian Audit Outcome Model Selection Using Normalising Flows -- 9 Bayesian Detection Of Unauthorized Expenditure Using Langevin and Hamiltonian Monte Carlo -- 10 Bayesian Neural Network Inference Of Motor Insurance Claims -- 11 Shadow and Adaptive Hamiltonian Monte Carlo Methods For Calibrating The Nelson and Siegel Model -- 12 Static and Dynamic Nested Sampling For Yield Curve Model Selection -- 13 A Bayesian Investment Analyst On The Johannesburg Stock Exchange -- 14 Conclusions to Bayesian Machine Learning In Quantitative Finance.
520 $a 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. The book covers a broad range of modelling approaches, including Bayesian neural networks, Gaussian processes and Markov Chain Monte Carlo methods. It also discusses the utility of Bayesian inference in quantitative finance and discusses future research goals in the applications of Bayesian machine learning in quantitative finance. Chapters are rooted in the theory of quantitative finance and machine learning while also outlining a range of practical considerations for implementing Bayesian techniques into real-world quantitative finance problems. This book is ideal for graduate researchers and practitioners at the intersection of machine learning and quantitative finance, as well as those working in computational statistics and computer science more broadly. 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 is an author of the machine learning book entitled "Hamiltonian Monte Carlo Methods in Machine Learning". Rendani Mbuvha is Associate Professor of Actuarial Science in the Department of Mathematics at the University of Manchester, UK. He is a fellow of the Institute and Faculty of Actuaries and Co-Founder of AfriClimate AI. 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. 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 has published 27 books in artificial intelligence and related areas.
650 0 $a Bayesian statistical decision theory. $3 182005
650 0 $a Finance $x Statistical methods. $3 240075
650 0 $a Finance $x Decision making $x Data processing. $3 569127
650 0 $a Machine learning $x Statistical methods. $3 305185
650 1 4 $a Corporate Finance. $2 swd $3 406349
650 2 4 $a Quantitative Economics. $3 913171
650 2 4 $a Computer Science. $3 212513
650 2 4 $a Probability Theory. $3 913371
700 1 $a Mbuvha, Rendani. $3 998606
700 1 $a Marwala, Tshilidzi. $3 470478
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-88431-3
950 $a Economics and Finance (SpringerNature-41170)