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searching for Hamiltonian Monte Carlo 8 found (16 total)

PyMC (1,439 words) [view diff] exact match in snippet view article find links to article

MCMC-based algorithms: No-U-Turn sampler (NUTS), a variant of Hamiltonian Monte Carlo and PyMC's default engine for continuous variables Metropolis–Hastings

Negative log predictive density (501 words) [view diff] case mismatch in snippet view article find links to article

Heinonen, Markus, et al. "Non-stationary gaussian process regression with hamiltonian monte carlo." Artificial Intelligence and Statistics. PMLR, 2016.

Leapfrog integration (1,581 words) [view diff] exact match in snippet view article find links to article

a symplectic integrator, leapfrog integration is also used in Hamiltonian Monte Carlo, a method for drawing random samples from a probability distribution

Bayesian network (6,456 words) [view diff] exact match in snippet view article find links to article

Bayesian inference using the No-U-Turn sampler (NUTS), a variant of Hamiltonian Monte Carlo. PyMC3 – A Python library implementing an embedded domain specific

Mark Girolami (718 words) [view diff] exact match in snippet view article find links to article

Mark; Calderhead, Ben (2011-03-01). "Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods". Journal of the Royal Statistical Society Series B: Statistical

Radford M. Neal (693 words) [view diff] exact match in snippet view article find links to article

Lan, Shiwei; Johnson, Wesley O.; Neal, Radford M. (2014). "Split Hamiltonian Monte Carlo". Statistics and Computing. 24 (3): 339–349. arXiv:1106.5941. doi:10

ArviZ (861 words) [view diff] exact match in snippet view article find links to article

PMID 31675792. S2CID 207834500. Zhou, Guangyao (2019). "Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables". arXiv:1909.04852

Exponential integrator (3,375 words) [view diff] exact match in snippet view article find links to article

Michels, Dominik L.; Sha, Fei (2015). "Exponential Integration for Hamiltonian Monte Carlo". Proceedings of the 32nd International Conference on Machine Learning