HMC Explained Without the Physics Metaphor
💡Learn why HMC works from probability and MCMC fundamentals—not just a physics analogy.
⚡ 30-Second TL;DR
What Changed
Introduces HMC by adding an auxiliary variable rather than starting with a physics analogy.
Why It Matters
The notes may make HMC easier to learn for practitioners who find physics-based explanations unintuitive. They can also serve as a conceptual reference when debugging or designing probabilistic inference workflows.
What To Do Next
Read the Zenodo notes and implement a small HMC sampler on a two-dimensional Gaussian to verify reversibility and volume preservation numerically.
Key Points
- •Introduces HMC by adding an auxiliary variable rather than starting with a physics analogy.
- •Explains how Hamiltonian dynamics and leapfrog integration construct practical MCMC transitions.
- •Discusses reversibility and volume preservation as key properties behind HMC correctness.
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Original source: Reddit r/MachineLearning ↗
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