Exact approximate Monte Carlo methods for complex Bayesian inference (research costs) (EAMC)
Main funder
Funder's project number: 312605
Funds granted by main funder (€)
- 140 000,00
Funding program
Project timetable
Project start date: 01/09/2017
Project end date: 31/08/2019
Summary
Bayesian statistics is a flexible and coherent framework to model different types of phenomena involving uncertainty. The applicability of Bayesian methods is often constrained by computational challenges, which can often be expressed in terms of high-dimensional and difficult integrals. Such situations may occur for example in time-series analysis, inverse problems and design of experiments.
The project focuses on an emerging class of computational methods, which are more efficient than earlier methods in complex situations, such as the above mentioned application domains. The research questions span from theoretical and methodological questions to applications. If succesful, the outcome of the project is research findings that lead into new more efficient computational methods, and open doors for new types of more challenging applications for Bayesian statistics.
The project focuses on an emerging class of computational methods, which are more efficient than earlier methods in complex situations, such as the above mentioned application domains. The research questions span from theoretical and methodological questions to applications. If succesful, the outcome of the project is research findings that lead into new more efficient computational methods, and open doors for new types of more challenging applications for Bayesian statistics.
Principal Investigator
Primary responsible unit
Related publications and other outputs
1 of 2
- Efficient Bayesian generalized linear models with time-varying coefficients : The walker package in R (2022) Helske, Jouni; A1; OA
- bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R (2021) Helske, Jouni; et al.; A1; OA
- Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions (2021) Chada, Neil K.; et al.; A1; OA
- A Bayesian Reconstruction of a Historical Population in Finland, 1647–1850 (2020) Voutilainen, Miikka; 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
- Mixture Hidden Markov Models for Sequence Data : The seqHMM Package in R (2019) Helske, Satu; et al.; A1; OA
