Hamiltonian Monte Carlo Methods in Machine Learning 1st Edition

Hamiltonian Monte Carlo Methods in Machine Learning, 1st Edition by Tshilidzi Marwala, Rendani Mbuvha, and Wilson Tsakane Mongwe is a modern, authoritative guide to one of the most powerful sampling techniques for Bayesian inference. This clear, engaging volume is written for graduate students, researchers, and data scientists who need robust, scalable solutions for sampling in high-dimensional models.

Begin with a compelling overview of Hamiltonian Monte Carlo (HMC) theory and the intuition behind Hamiltonian dynamics, then move into practical algorithmic development: gradient-based proposals, leapfrog integrators, tuning strategies, and diagnostics for convergence and efficiency. The authors balance mathematical rigor with accessible explanations, making complex concepts like symplectic integrators and mass matrix adaptation understandable without sacrificing depth.

Discover why HMC outperforms traditional Markov Chain Monte Carlo in large-scale machine learning problems and how to apply it to Bayesian neural networks, hierarchical models, and probabilistic graphical models. Real-world examples and implementation-focused pseudocode help bridge theory and practice, equipping readers to implement HMC in Python, R, or other scientific computing environments.

Ideal for audiences across academia and industry—from research labs in Europe and North America to data science teams in Africa and Asia—this book emphasizes reproducible, computationally efficient approaches suited to contemporary machine learning challenges. Whether you’re refining model inference, speeding up posterior exploration, or teaching advanced statistical methods, this title becomes an essential reference.

Order your copy today to enhance your toolkit with a state-of-the-art resource on Hamiltonian Monte Carlo. Gain the theoretical foundation and practical strategies needed to tackle complex Bayesian problems with confidence.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.