MCMC using Hamiltonian dynamics¶
Neal, R. M. (2011). MCMC using Hamiltonian dynamics. Handbook of Markov Chain Monte Carlo.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Monte Carlo Simulation
- T4 — General-purpose simplicity vs specialized-algorithm performance. The vanilla Metropolis-Hastings algorithm is broadly applicable but often inefficient for high-dimensional or complex targets; specialized algorithms (Hamiltonian Monte Carlo for smooth high-dimensional targets, slice sampling, reversible-jump MCMC for trans-dimensional models, particle filters for state-space models) can dramatically improve convergence but require domain expertise to choose and tune
This sourceNeal HMC gradient-informed proposal high-dimensional efficiency.
- T4 — General-purpose simplicity vs specialized-algorithm performance. The vanilla Metropolis-Hastings algorithm is broadly applicable but often inefficient for high-dimensional or complex targets; specialized algorithms (Hamiltonian Monte Carlo for smooth high-dimensional targets, slice sampling, reversible-jump MCMC for trans-dimensional models, particle filters for state-space models) can dramatically improve convergence but require domain expertise to choose and tune
Verification¶
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Registry ID ref:6d93b92ddb61 · see in the full table