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Ensemble Kalman inversion for general likelihoods

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  • Duffield, Samuel
  • Singh, Sumeetpal S.

Abstract

In this letter we generalise Ensemble Kalman inversion techniques to general Bayesian models where previously they were restricted to additive Gaussian likelihoods — all in the difficult setting where the likelihood can be sampled from, but its density not necessarily evaluated.

Suggested Citation

  • Duffield, Samuel & Singh, Sumeetpal S., 2022. "Ensemble Kalman inversion for general likelihoods," Statistics & Probability Letters, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:stapro:v:187:y:2022:i:c:s0167715222000967
    DOI: 10.1016/j.spl.2022.109523
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    References listed on IDEAS

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    1. Pierre Del Moral & Arnaud Doucet & Ajay Jasra, 2006. "Sequential Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 411-436, June.
    2. Ajay Jasra & David A. Stephens & Arnaud Doucet & Theodoros Tsagaris, 2011. "Inference for Lévy‐Driven Stochastic Volatility Models via Adaptive Sequential Monte Carlo," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(1), pages 1-22, March.
    3. Matti Vihola & Jordan Franks, 2020. "On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction," Biometrika, Biometrika Trust, vol. 107(2), pages 381-395.
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