# Generalized Bayesian Computation

October 3, 2019 — April 28, 2022

approximation

Bayes

functional analysis

generative

how do science

measure

metrics

Monte Carlo

nonparametric

probability

statistics

stochastic processes

Placeholder.

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]*.