3 citations · 8 across the 15 of their papers we have counts for
Showing 2016Show all
3 papers · 1 filter
stat.CO2016
Modified Cholesky Riemann Manifold Hamiltonian Monte Carlo: Exploiting Sparsity for Fast Sampling of High-dimensional Targets
Tore Selland Kleppe
Riemann manifold Hamiltonian Monte Carlo (RMHMC) has the potential to produce high-quality Markov chain Monte Carlo-output even for very challenging target distributions. To this e…
stat.CO2016★ 2 cited
On the application of higher order symplectic integrators in Hamiltonian Monte Carlo
Janne Mannseth, Tore Selland Kleppe, Hans J. Skaug
We explore the construction of new symplectic numerical integration schemes to be used in Hamiltonian Monte Carlo and study their efficiency. Two integration schemes from Blanes et…
stat.CO2016
The Gibbs Sampler with Particle Efficient Importance Sampling for State-Space Models
Oliver Grothe, Tore Selland Kleppe, Roman Liesenfeld
We consider Particle Gibbs (PG) as a tool for Bayesian analysis of non-linear non-Gaussian state-space models. PG is a Monte Carlo (MC) approximation of the standard Gibbs procedur…