On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction
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Cited by:
- Henri Pesonen & Umberto Simola & Alvaro Köhn‐Luque & Henri Vuollekoski & Xiaoran Lai & Arnoldo Frigessi & Samuel Kaski & David T. Frazier & Worapree Maneesoonthorn & Gael M. Martin & Jukka Corander, 2023. "ABC of the future," International Statistical Review, International Statistical Institute, vol. 91(2), pages 243-268, August.
- George Karabatsos, 2023. "Approximate Bayesian computation using asymptotically normal point estimates," Computational Statistics, Springer, vol. 38(2), pages 531-568, June.
- Matti Vihola & Jouni Helske & Jordan Franks, 2020. "Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1339-1376, December.
- Duffield, Samuel & Singh, Sumeetpal S., 2022. "Ensemble Kalman inversion for general likelihoods," Statistics & Probability Letters, Elsevier, vol. 187(C).
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Keywords
Adaptive algorithm; Approximate Bayesian computation; Confidence interval; Importance sampling; Markov chain Monte Carlo; Tolerance choice;All these keywords.
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