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ORCIDTore Selland Kleppe
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2020 – today
- 2024
[j9]Jimmy Huy Tran, Tore Selland Kleppe:
Tuning diagonal scale matrices for HMC. Stat. Comput. 34(6): 196 (2024)- 2023
- [i2]Nawaf Bou-Rabee, Tore Selland Kleppe:
Randomized Runge-Kutta-Nyström. CoRR abs/2310.07399 (2023) - 2022
- [j8]Tore Selland Kleppe
:
Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Rates. J. Comput. Graph. Stat. 31(4): 1238-1253 (2022) - 2021
- [j7]Kjartan Kloster Osmundsen, Tore Selland Kleppe, Atle Oglend:
MCMC for Markov-switching models - Gibbs sampling vs. marginalized likelihood. Commun. Stat. Simul. Comput. 50(3): 669-690 (2021) - [j6]Kjartan Kloster Osmundsen, Tore Selland Kleppe, Roman Liesenfeld:
Importance Sampling-Based Transport Map Hamiltonian Monte Carlo for Bayesian Hierarchical Models. J. Comput. Graph. Stat. 30(4): 906-919 (2021) - 2020
- [i1]Berent Ånund Strømnes Lunde, Tore Selland Kleppe:
agtboost: Adaptive and Automatic Gradient Tree Boosting Computations. CoRR abs/2008.12625 (2020)
2010 – 2019
- 2018
- [j5]Janne Mannseth, Tore Selland Kleppe, Hans J. Skaug:
On the application of improved symplectic integrators in Hamiltonian Monte Carlo. Commun. Stat. Simul. Comput. 47(2): 500-509 (2018) - [j4]Tore Selland Kleppe:
Modified Cholesky Riemann Manifold Hamiltonian Monte Carlo: exploiting sparsity for fast sampling of high-dimensional targets. Stat. Comput. 28(4): 795-817 (2018) - 2016
- [j3]Tore Selland Kleppe, Hans J. Skaug:
Bandwidth selection in pre-smoothed particle filters. Stat. Comput. 26(5): 1009-1024 (2016) - 2014
- [j2]Tore Selland Kleppe, Roman Liesenfeld:
Efficient importance sampling in mixture frameworks. Comput. Stat. Data Anal. 76: 449-463 (2014) - 2012
- [j1]Tore Selland Kleppe, Hans Julius Skaug:
Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling. Comput. Stat. Data Anal. 56(11): 3105-3119 (2012)
Coauthor Index
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