Wilson Tsakane Mongwe: Bayesian Machine Learning in Quantitative Finance, Gebunden
Bayesian Machine Learning in Quantitative Finance
- Theory and Practical Applications
(soweit verfügbar beim Lieferanten)
- Verlag:
- Springer, 06/2025
- Einband:
- Gebunden
- Sprache:
- Englisch
- ISBN-13:
- 9783031884306
- Artikelnummer:
- 12333163
- Umfang:
- 372 Seiten
- Gewicht:
- 591 g
- Maße:
- 216 x 153 mm
- Stärke:
- 25 mm
- Erscheinungstermin:
- 22.6.2025
- Hinweis
-
Achtung: Artikel ist nicht in deutscher Sprache!
Klappentext
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.
Biografie (Tshilidzi Marwala)
Tshilidzi Marwala is the Executive Dean of the Faculty of Engineering and the Built Environment at the University of Johannesburg. He was previously the Adhominem Professor of Electrical Engineering as well as the Carl and Emily Fuchs Chair of Systems and Control Engineering at the University of the Witwatersrand. He is a Fellow of the Royal Society of Arts as well as the Royal Statistical Society. He holds a PhD in Engineering from the University of Cambridge and a PLD from Harvard University in the USA. He was a post-doctoral research associate at Imperial College working in the general area of computational intelligence. He has been a visiting fellow at Harvard University and Cambridge University. His research interests include the application of computational intelligence to mechanical. civil, aerospace and biomedical engineering. Professor Marwala has made fundamental contributions to engineering including the development of the concept of pseudo-modal energies and the development of Bayesian framework for solving engineering problems such as finite element model updating. He has supervised 40 masters and PhD students many of whom have proceeded to distinguish themselves at universities such as Harvard, Oxford and Cambridge. He has published over 200 papers in journals such as the American Institute of Aeronautics and Astronautics Journal, proceedings and book chapters. He has published two books: Computational Intelligence for Modelling Complex Systems published by Research India Publications as well as Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques published by the IGI Global Publications (New York). His work has appeared in prestigious publications such as New Scientist. He is a senior member of the IEEE.