Monte Carlo -menetelmät
http://www.yso.fi/onto/yso/p6361
Liittyvät julkaisut
1/2
- 0.1-10 MeV Neutron Soft Error Rate in Accelerator and Atmospheric Environments (2021) Cecchetto, Matteo; et al.; A1; OA
- Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions (2021) Chada, Neil K.; et al.; A1; OA
- An Updated Guideline for Assessing Discriminant Validity (2020) Rönkkö, Mikko; et al.; A1; OA
- Coupled conditional backward sampling particle filter (2020) Lee, Anthony; et al.; A1; OA
- Ergonomic and Reliable Bayesian Inference with Adaptive Markov Chain Monte Carlo (2020) Vihola, Matti; A3; 978-1-118-44511-2
- Importance sampling correction versus standard averages of reversible MCMCs in terms of the asymptotic variance (2020) Franks, Jordan; et al.; A1; OA
- Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo (2020) Vihola, Matti; et al.; A1; OA
- Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods (2020) Pihlajamäki, Antti; et al.; A1; OA
- On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction (2020) Vihola, Matti; et al.; A1; OA
- CLEASE : A versatile and user-friendly implementation of Cluster Expansion method (2019) Chang, Jin Hyun; et al.; A1; OA
