Hamiltonian Monte Carlo Methods in Machine Learning by Tshilidzi Marwala 9780443190360 0443190364

Hamiltonian Monte Carlo Methods in Machine Learning Tshilidzi Marwala – Ebook Instant Download/Delivery ISBN(s): 9780443190353, 0443190356, 9780443190360, 0443190364

Product details:

• ISBN 10: 0443190364
• ISBN 13: 9780443190360
• Author: Tshilidzi Marwala

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.

Table contents:

1: Introduction to Hamiltonian Monte Carlo

2: Sampling benchmarks and performance metrics

3: Stochastic volatility Metropolis-Hastings

4: Quantum-inspired magnetic Hamiltonian Monte Carlo

5: Generalised magnetic and shadow Hamiltonian Monte Carlo

6: Shadow Magnetic Hamiltonian Monte Carlo

7: Adaptive Shadow Hamiltonian Monte Carlo

8: Adaptive noncanonical Hamiltonian Monte Carlo

9: Antithetic Hamiltonian Monte Carlo techniques

10: Bayesian neural network inference in wind speed nowcasting

11: A Bayesian analysis of the efficacy of Covid-19 lockdown measures

12: Probabilistic inference of equity option prices under jump-diffusion processes

13: Bayesian inference of local government audit outcomes

14: Conclusions

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