LEADER 05247nam 22006255 450 001 9911011778803321 005 20250622130216.0 010 $a3-031-88431-0 024 7 $a10.1007/978-3-031-88431-3 035 $a(MiAaPQ)EBC32174190 035 $a(Au-PeEL)EBL32174190 035 $a(CKB)39419171800041 035 $a(OCoLC)1525396056 035 $a(DE-He213)978-3-031-88431-3 035 $a(EXLCZ)9939419171800041 100 $a20250622d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBayesian Machine Learning in Quantitative Finance $eTheory and Practical Applications /$fby Wilson Tsakane Mongwe, Rendani Mbuvha, Tshilidzi Marwala 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Palgrave Macmillan,$d2025. 215 $a1 online resource (350 pages) 311 08$a3-031-88430-2 327 $a1 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. 330 $aThis 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. 606 $aBusiness enterprises$xFinance 606 $aEconometrics 606 $aComputer science 606 $aProbabilities 606 $aCorporate Finance 606 $aQuantitative Economics 606 $aComputer Science 606 $aProbability Theory 615 0$aBusiness enterprises$xFinance. 615 0$aEconometrics. 615 0$aComputer science. 615 0$aProbabilities. 615 14$aCorporate Finance. 615 24$aQuantitative Economics. 615 24$aComputer Science. 615 24$aProbability Theory. 676 $a332.01519542 700 $aMongwe$b Wilson Tsakane$01830180 701 $aMbuvha$b Rendani$01830181 701 $aMarwala$b Tshilidzi$0899934 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911011778803321 996 $aBayesian Machine Learning in Quantitative Finance$94400463 997 $aUNINA