Hamiltonian Monte Carlo Methods in Machine Learning
eBook - ePub

Hamiltonian Monte Carlo Methods in Machine Learning

  1. 220 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Hamiltonian Monte Carlo Methods in Machine Learning

About this book

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

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Yes, you can access Hamiltonian Monte Carlo Methods in Machine Learning by Tshilidzi Marwala,Rendani Mbuvha,Wilson Tsakane Mongwe in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Front Matter
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. Contents
  7. List of figures
  8. List of tables
  9. Authors
  10. Foreword
  11. Preface
  12. Nomenclature
  13. List of symbols
  14. List of Illustrations
  15. List of Tables
  16. 1 : Introduction to Hamiltonian Monte Carlo
  17. 2 : Sampling benchmarks and performance metrics
  18. 3 : Stochastic volatility Metropolis-Hastings
  19. 4 : Quantum-inspired magnetic Hamiltonian Monte Carlo
  20. 5 : Generalised magnetic and shadow Hamiltonian Monte Carlo
  21. 6 : Shadow Magnetic Hamiltonian Monte Carlo
  22. 7 : Adaptive Shadow Hamiltonian Monte Carlo
  23. 8 : Adaptive noncanonical Hamiltonian Monte Carlo
  24. 9 : Antithetic Hamiltonian Monte Carlo techniques
  25. 10 : Bayesian neural network inference in wind speed nowcasting
  26. 11 : A Bayesian analysis of the efficacy of Covid-19 lockdown measures
  27. 12 : Probabilistic inference of equity option prices under jump-diffusion processes
  28. 13 : Bayesian inference of local government audit outcomes
  29. 14 : Conclusions
  30. A : Separable shadow Hamiltonian
  31. B : ARD posterior variances
  32. C : ARD committee feature selection
  33. D : Summary of audit outcome literature survey
  34. References
  35. Index
  36. A