Markov chain Monte Carlo importance samplers for Bayesian models with intractable likelihoods
Main Author
Format
Theses
Doctoral thesis
Published
2019
Series
Subjects
ISBN
978-951-39-7738-2
Publisher
Jyväskylän yliopisto
The permanent address of the publication
https://urn.fi/URN:ISBN:978-951-39-7738-2Käytä tätä linkitykseen.
ISSN
2489-9003
Language
English
Published in
JYU Dissertations
Contains publications
- Artikkeli I: Vihola, Matti; Helske, Jouni; Franks, Jordan (2020). Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. Scandinavian Journal of Statistics, Early online. DOI: 10.1111/sjos.12492
- Artikkeli II: Franks, Jordan; Vihola, Matti (2020). Importance sampling correction versus standard averages of reversible MCMCs in terms of the asymptotic variance. Stochastic Processes and Their Applications, 130 (10), 6157-6183. DOI: 10.1016/j.spa.2020.05.006
- Artikkeli III: Franks, J.; Jasra, A.; Law, K. J. H. and Vihola, M. (2018). Unbiased inference for discretely observed hidden Markov model diffusions. Preprint. arXiv:1807.10259v4
- Artikkeli IV: Vihola, Matti; Franks, Jordan (2020). On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction. Biometrika, 107 (2), 381-395. DOI: 10.1093/biomet/asz078
License
Copyright© The Author & University of Jyväskylä