Mastering Hamiltonian Monte Carlo
Advanced Sampling Techniques for Machine Learning and Computational Statistics
Picture yourself confidently tackling the most challenging probabilistic models in machine learning, where traditional sampling methods fail and computational bottlenecks disappear. You'll move beyond the limitations of basic MCMC techniques, wielding the power of physics-inspired algorithms that slice through high-dimensional parameter spaces with unprecedented efficiency. This comprehensive guide transforms complex mathematical concepts into practical, implementable knowledge. You'll discover how Hamiltonian dynamics revolutionize sampling from posterior distributions, enabling you to work with models that were previously computationally intractable. Through clear explanations and hands-on examples, you'll master the art of leveraging gradient information to create sampling chains that explore parameter space more intelligently than ever before. Your journey progresses from fundamental concepts to advanced implementations, covering everything from basic HMC mechanics to sophisticated variants like NUTS (No-U-Turn Sampler). You'll learn to diagnose sampling problems, optimize performance, and adapt these techniques to your specific machine learning challenges. Real-world case studies demonstrate how HMC transforms Bayesian deep learning, hierarchical models, and complex statistical inference problems. By the final page, you'll possess the expertise to implement, tune, and troubleshoot Hamiltonian Monte Carlo methods across diverse applications. You'll understand not just how these algorithms work, but why they work, giving you the insight needed to push the boundaries of what's computationally possible in your own research and projects.
Est. Length
300 pages (17 chapters)
Language
English
Publication date
Sep 28, 2025
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