Generalized Bayesian Computation

October 3, 2019 — April 28, 2022

functional analysis
how do science
Monte Carlo
stochastic processes
Figure 1


Just saw a presentation of Dellaporta et al. (2022).

I am not sure how any of the results are specific to that very impressive paper, but she attributes prior work to Fong, Lyddon, and Holmes (2019);Lyddon, Walker, and Holmes (2018);Matsubara et al. (2021);Pacchiardi and Dutta (2022);Schmon, Cannon, and Knoblauch (2021). Combines bootstrap, Bayes nonparametrics, MMD, simulation based inference in an M-open setting.

Clearly there is some interesting stuff going on here. Perhaps this introductory post will be a good start: Generalizing Bayesian Inference.

1 References

Dellaporta, Knoblauch, Damoulas, et al. 2022. Robust Bayesian Inference for Simulator-Based Models via the MMD Posterior Bootstrap.” arXiv:2202.04744 [Cs, Stat].
Fong, Lyddon, and Holmes. 2019. Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap.” arXiv:1902.03175 [Cs, Stat].
Galvani, Bardelli, Figini, et al. 2021. A Bayesian Nonparametric Learning Approach to Ensemble Models Using the Proper Bayesian Bootstrap.” Algorithms.
Lyddon, Walker, and Holmes. 2018. Nonparametric Learning from Bayesian Models with Randomized Objective Functions.” In Proceedings of the 32nd International Conference on Neural Information Processing Systems. NIPS’18.
Matsubara, Knoblauch, Briol, et al. 2021. Robust Generalised Bayesian Inference for Intractable Likelihoods.” arXiv:2104.07359 [Math, Stat].
Pacchiardi, and Dutta. 2022. Generalized Bayesian Likelihood-Free Inference Using Scoring Rules Estimators.” arXiv:2104.03889 [Stat].
Schmon, Cannon, and Knoblauch. 2021. Generalized Posteriors in Approximate Bayesian Computation.” arXiv:2011.08644 [Stat].