Exact approximate Monte Carlo methods for complex Bayesian inference
Main funder
Funder's project number: 274740
Funds granted by main funder (€)
- 434 485,00
Funding program
Project timetable
Project start date: 01/09/2014
Project end date: 31/08/2019
Principal Investigator
Other persons related to this project (JYU)
Primary responsible unit
Related publications and other outputs
- 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 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
- On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction (2020) Vihola, Matti; et al.; A1; OA
- Prediction of leukocyte counts during paediatric acute lymphoblastic leukaemia maintenance therapy (2019) Karppinen, Santeri; et al.; A1; OA
- Graphical model inference : Sequential Monte Carlo meets deterministic approximations (2018) Lindsten, Fredrik; et al.; A4; OA
- Theoretical and methodological aspects of MCMC computations with noisy likelihoods (2018) Andrieu, Christophe; et al.; A3; OA
- Unbiased Estimators and Multilevel Monte Carlo (2018) Vihola, Matti; A1; OA