Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling. - Tore Selland Kleppe; Hans Julius Skaug (Comput. Stat. Data Anal., 2012)

Efficient importance sampling in mixture frameworks. - Tore Selland Kleppe; Roman Liesenfeld (Comput. Stat. Data Anal., 2014)

Bandwidth selection in pre-smoothed particle filters. - Tore Selland Kleppe; Hans J. Skaug (Stat. Comput., 2016)

On the application of improved symplectic integrators in Hamiltonian Monte Carlo. - Janne Mannseth; Tore Selland Kleppe; Hans J. Skaug (Commun. Stat. Simul. Comput., 2018)

Modified Cholesky Riemann Manifold Hamiltonian Monte Carlo: exploiting sparsity for fast sampling of high-dimensional targets. - Tore Selland Kleppe (Stat. Comput., 2018)

agtboost: Adaptive and Automatic Gradient Tree Boosting Computations. - Berent Ånund Strømnes Lunde; Tore Selland Kleppe (CoRR, 2020)

MCMC for Markov-switching models - Gibbs sampling vs. marginalized likelihood. - Kjartan Kloster Osmundsen; Tore Selland Kleppe; Atle Oglend (Commun. Stat. Simul. Comput., 2021)

Importance Sampling-Based Transport Map Hamiltonian Monte Carlo for Bayesian Hierarchical Models. - Kjartan Kloster Osmundsen; Tore Selland Kleppe; Roman Liesenfeld (J. Comput. Graph. Stat., 2021)

Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Rates. - Tore Selland Kleppe (J. Comput. Graph. Stat., 2022)

Randomized Runge-Kutta-Nyström. - Nawaf Bou-Rabee; Tore Selland Kleppe (CoRR, 2023)

Tuning diagonal scale matrices for HMC. - Jimmy Huy Tran; Tore Selland Kleppe (Stat. Comput., 2024)

Numerical Generalized Randomized HMC processes for restricted domains. - Tore Selland Kleppe; Roman Liesenfeld (Stat. Comput., 2025)