Scalable methods for reliable Bayesian inference (SCALEBAYES) (SCALEBAYES)
Main funder
Funder's project number: 315619
Funds granted by main funder (€)
- 522 382,00
Funding program
Project timetable
Project start date: 01/09/2018
Project end date: 31/08/2022
Summary
Scalable methods for reliable Bayesian inference (SCALEBAYES)
Principal Investigator
Primary responsible unit
Follow-up groups
Related publications and other outputs
1 of 2
- Conditional particle filters with bridge backward sampling (2023) Karppinen, Santeri; et al.; A1; OA
- Identifying territories using presence-only citizen science data : An application to the Finnish wolf population (2022) Karppinen, Santeri; et al.; A1; OA
- Non-linear state-space methods for Bayesian time series modelling (2022) Karppinen, Santeri; G5; OA; 978-951-39-9226-2
- On resampling schemes for particle filters with weakly informative observations (2022) Chopin, Nicolas; et al.; A1; OA
- bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R (2021) Helske, Jouni; et al.; A1; OA
- Conditional particle filters with diffuse initial distributions (2021) Karppinen, Santeri; et al.; A1; OA
- Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions (2021) Chada, Neil K.; 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 type estimators based on approximate marginal Markov chain Monte Carlo (2020) Vihola, Matti; et al.; A1; OA
