3 citations · 8 across the 15 of their papers we have counts for
15 papers · 1 filter
Locally adaptive step sizes for Hamiltonian Monte Carlo methods
Jimmy Huy Tran, Tore Selland Kleppe, Nawaf Bou-Rabee
Exact reversible locally adaptive step sizes are introduced by Bou-Rabee et al. (2026) in the context of the No-U-Turn Hamiltonian Monte Carlo (HMC) sampler for the purpose of high…
The Within-Orbit Adaptive Leapfrog No-U-Turn Sampler
Nawaf Bou-Rabee, Bob Carpenter, Tore Selland Kleppe +1
Locally adapting parameters within Markov chain Monte Carlo methods while preserving reversibility is notoriously difficult. The success of the No-U-Turn Sampler (NUTS) largely ste…
Numerical Generalized Randomized Hamiltonian Monte Carlo for piecewise smooth target densities
Jimmy Huy Tran, Tore Selland Kleppe
Traditional gradient-based sampling methods, like standard Hamiltonian Monte Carlo, require that the desired target distribution is continuous and differentiable. This limits the t…
Tuning diagonal scale matrices for HMC
Jimmy Huy Tran, Tore Selland Kleppe
Three approaches for adaptively tuning diagonal scale matrices for HMC are discussed and compared. The common practice of scaling according to estimated marginal standard deviation…
Numerical Generalized Randomized HMC processes for restricted domains
Tore Selland Kleppe, Roman Liesenfeld
We propose a generic approach for numerically efficient simulation from analytically intractable distributions with constrained support. Our approach relies upon Generalized Random…
Log-density gradient covariance and automatic metric tensors for Riemann manifold Monte Carlo methods
Tore Selland Kleppe
A metric tensor for Riemann manifold Monte Carlo particularly suited for non-linear Bayesian hierarchical models is proposed. The metric tensor is built from symmetric positive sem…
Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Rates
Tore Selland Kleppe
Numerical Generalized Randomized Hamiltonian Monte Carlo is introduced, as a robust, easy to use and computationally fast alternative to conventional Markov chain Monte Carlo metho…
Analyzing Commodity Futures Using Factor State-Space Models with Wishart Stochastic Volatility
Tore Selland Kleppe, Roman Liesenfeld, Guilherme Valle Moura +1
We propose a factor state-space approach with stochastic volatility to model and forecast the term structure of future contracts on commodities. Our approach builds upon the dynami…
Importance Sampling-based Transport Map Hamiltonian Monte Carlo for Bayesian Hierarchical Models
Kjartan Kloster Osmundsen, Tore Selland Kleppe, Roman Liesenfeld
We propose an importance sampling (IS)-based transport map Hamiltonian Monte Carlo procedure for performing full Bayesian analysis in general nonlinear high-dimensional hierarchica…
Saddlepoint-adjusted inversion of characteristic functions
Berent Å. S. Lunde, Tore S. Kleppe, Hans J. Skaug
For certain types of statistical models, the characteristic function (Fourier transform) is available in closed form, whereas the probability density function has an intractable fo…
Dynamically rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Models
Tore Selland Kleppe
Dynamically rescaled Hamiltonian Monte Carlo (DRHMC) is introduced as a computationally fast and easily implemented method for performing full Bayesian analysis in hierarchical sta…
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…
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…
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…
Bandwidth Selection In Pre-Smoothed Particle Filters
Tore Selland Kleppe, Hans Julius Skaug
For the purpose of maximum likelihood estimation of static parameters, we apply a kernel smoother to the particles in the standard SIR filter for non-linear state space models with…