The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo | TransferLab — appliedAI Institute

Reference

The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo, Matthew D. Hoffman, Andrew Gelman. Journal of Machine Learning Research(2014)

Content citing this item

Hamiltonian Monte Carlo Sampling

Metropolis, Slice or Gibbs sampling tend to be inefficient when applied in high dimensions due to their local random walk behaviour. …

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Probabilistic Models

Dec 3, 2020

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