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.
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