Et profilbilde

Professor
Tore Selland Kleppe { "honorific-suffix": "Professor", "fn": "Tore Selland Kleppe", "tel": "Telephone: +47 51831717", "email": "tore.kleppe@uis.no" }

Faculty Faculty of Science and Technology
Department Department of Mathematics and Physics
Room KE E-546

Research fields

Computational Statistics, Bayesian Statistics, Econometrics, Dynamic Latent Variable Models

Selected publications

Research in progress

Work experience

Scientific publications (from Cristin)

  • Grothe, Oliver; Kleppe, Tore Selland; Liesenfeld, Roman (2016). Bayesian Analysis in Non-linear Non-Gaussian State-Space Models using Particle Gibbs. arXiv. 40 p.
  • Kleppe, Tore Selland (2010). Integrating out the unknown: Four papers on likelihood estimation in non-Gaussian latent variable models. Universitetet i Bergen. 200 p.
  • Kleppe, Tore Selland; Green, William; Yu, Jun; Hill, R. Carter; Skaug, Hans Julius (2010). Maximum Simulated Likelihood Methods and Applications. Emerald Group Publishing Limited. ISBN 978-0-85724-149-8.
  • Kleppe, Tore Selland (2019). Hamiltonian Monte Carlo for Bayesian Hierarchical Models. 2019-06-18 - 2019-06-20.
  • Kleppe, Tore Selland (2018). Dynamically Rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Models. 2018-11-30.
  • Lunde, Berent Ånund Strømnes; Kleppe, Tore Selland; Skaug, Hans Julius (2018). Saddlepoint adjusted inversion of characteristic functions. CFE-CMStatistics, City University of Honk Kong, and IFMSE; 2018-06-18 - 2018-06-21.
  • Osmundsen, Kjartan Kloster; Kleppe, Tore Selland; Liesenfeld, Roman (2018). Pseudo-Marginal Hamiltonian Monte Carlo with Efficient Importance Sampling. LMS/CRISM, University of Warwick; 2018-07-09 - 2018-07-13.
  • Osmundsen, Kjartan Kloster; Kleppe, Tore Selland; Liesenfeld, Roman (2018). Pseudo-Marginal Hamiltonian Monte Carlo with Efficient Importance Sampling. CFE-CMStatistics,City University of Hong Kong; 2018-06-18 - 2018-06-21.
  • Kleppe, Tore Selland; Liesenfeld, Roman; Grothe, Oliver (2016). Bayesian Analysis in Non-linear Non-Gaussian State-Space Models using Particle Gibbs. The Nordic statistical societies; 2016-06-27 - 2016-06-30.
  • Kleppe, Tore Selland (2015). Adaptive step length selection for Hessian-based manifold Langevin samplers. Norsk statistisk forening; 2015-06-15 - 2015-06-18.
  • Kleppe, Tore Selland (2014). Adaptive step length selection for Hessian-based manifold Langevin samplers. 2014-12-17 - 2014-12-18.
  • Kleppe, Tore Selland; Skaug, Hans J. (2014). Bandwidth Selection In Pre-Smoothed Particle Filters. 2014-11-11.
  • Kleppe, Tore Selland; Berentsen, Geir Drage (2016). localgauss. The Comprehensive R Archive Network.