Hamiltonian Monte Carlo Methods in Machine Learning

Tshilidzi Marwala, Wilson Tsakane Mongwe, Rendani Mbuvha

Research output: Book/ReportBookpeer-review

Abstract

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.

Original languageEnglish
PublisherElsevier
Number of pages190
ISBN (Electronic)9780443190353
ISBN (Print)9780443190360
DOIs
Publication statusPublished - 1 Jan 2023

ASJC Scopus subject areas

  • General Computer Science

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