•1 min read•from Machine Learning
Notes on Hamiltonian Monte Carlo from a purely probabilistic perspective [P]
I’ve been studying Hamiltonian Monte Carlo and wrote a set of notes explaining HMC without relying on the usual physics-based motivation.
The notes develop HMC from a probabilistic/MCMC perspective, starting from introducing an auxiliary variable, constructing the corresponding Markov chain, and then covering Hamiltonian dynamics, leapfrog integration, reversibility and volume preservation.
The goal was to understand why HMC works rather than treating the physics analogy as a prerequisite.
I’m sharing them here in case they’re useful to others learning HMC. I’d also appreciate any feedback, particularly if you notice errors or places where the exposition could be improved.
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Tagged with
#Hamiltonian Monte Carlo
#HMC
#MCMC
#Markov chain
#Probabilistic perspective
#Auxiliary variable
#Hamiltonian dynamics
#Leapfrog integration
#Reversibility
#Volume preservation
#Physics analogy
#Statistical inference
#Bayesian inference
#Sampling methods
#Machine Learning
#Numerical methods
#Monte Carlo
#Stochastic processes
#Mathematical physics
#Computational statistics