Hamiltonian Monte Carlo Methods in Machine Learning
Language: English
Published by Academic Press, 2023
- Softcover
- New
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- Title
- Hamiltonian Monte Carlo Methods in Machine Learning
- Author
- Marwala, Tshilidzi; Mbuvha, Rendani; Mongwe, Wilson Tsalane
- Publisher
- Academic Press
- Publication year
- 2023
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 0443190356
- ISBN 13
- 9780443190353
Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC methods with improvements and speedup. Lastly, the authors address the critical issues of variance reduction for parameter estimates of numerous HMC based samplers. The book offers a comprehensive introduction to Hamiltonian Monte Carlo methods and provides a cutting-edge exposition of the current pathologies of HMC-based methods in both tuning, scaling and sampling complex real-world posteriors. These are mainly in the scaling of inference (e.g., Deep Neural Networks), tuning of performance-sensitive sampling parameters and high sample autocorrelation.
Other sections provide numerous solutions to potential pitfalls, presenting advanced HMC methods with applications in renewable energy, finance and image classification for biomedical applications. Readers will get acquainted with both HMC sampling theory and algorithm implementation.
- Provides in-depth analysis for conducting optimal tuning of Hamiltonian Monte Carlo (HMC) parameters
- Presents readers with an introduction and improvements on Shadow HMC methods as well as non-canonical HMC methods
- Demonstrates how to perform variance reduction for numerous HMC-based samplers
- Includes source code from applications and algorithms
"Synopsis" may belong to another edition of this title.
About the Author
engineering, computer science, finance, social science and medicine. He has supervised 28 Doctoral students published 15 books in artificial intelligence (one translated into Chinese), over 300 papers in journals, proceedings, book chapters and magazines and holds five patents. He is an associate editor of the International Journal of Systems Science (Taylor and Francis Publishers). He has been a visiting scholar at Harvard University, University of California at Berkeley, Wolfson College of the University of Cambridge, Nanjing Tech University and Silesian University of Technology in Poland. His opinions have appeared in the New Scientist, The Economist, Time Magazine, BBC, CNN and the Oxford Union. Dr. Marwala is the author of Rational Machines and Artificial Intelligence from Elsevier Academic Press.
Dr. Rendani Mbuvha is a lecturer in Statistics and Actuarial Science at the University of Witwatersrand, Johannesburg, South Africa. He is a qualified Actuary and a holder of the Chartered Enterprise Risk Actuary designation. He holds a BSc with Honors in Actuarial Science and Statistics from the University of Cape Town, an MSc in Machine Learning from KTH, Royal Institute of Technology in Sweden, and a Ph.D. in Probabilistic Parameter Inference at the University of Johannesburg. He was a recipient of the Google Ph.D. fellowship for his research at the University of Johannesburg. He has previously served in various analytics and actuarial roles in large financial services and AI consulting organizations in both South Africa and Sweden.
Dr Wilson Tsakane Mongwe holds a PhD in Artificial Intelligence from the University of Johannesburg and was selected as a Google PhD Fellow in Machine Learning, one of only sixteen recipients worldwide. He serves as a Visiting Lecturer at the University of the Witwatersrand, where he co-supervises postgraduate research and contributes to teaching in machine learning and computational methods. His academic work centers on scalable Bayesian inference, including Hamiltonian Monte Carlo, Laplace approximation, and variational techniques, with applications spanning computational finance, risk modelling, and sustainable development. He has authored two academic books, Hamiltonian Monte Carlo Methods in Machine Learning (Elsevier, 2023) and Bayesian Machine Learning in Quantitative Finance (Springer, 2025), alongside more than twenty peer-reviewed publications appearing in venues such as IEEE Access, PLoS ONE, and NeurIPS workshops. His research bridges probabilistic machine learning theory with real-world decision-making challenges across financial, environmental, and governance domains.
"About the title" may belong to another edition of this title.
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