Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Bayesian Inference for Simulator-based Models via the MMD
  Posterior Bootstrap   Simulator-based models are models for which the likelihood is intractable but
simulation of synthetic data is possible. They are often used to describe
complex real-world phenomena, and as such can often be misspecified in
practice. Unfortunately, existing Bayesian approaches for simulators are known
to perform poorly in those cases. In this paper, we propose a novel algorithm
based on the posterior bootstrap and maximum mean discrepancy estimators. This
leads to a highly-parallelisable Bayesian inference algorithm with strong
robustness properties. This is demonstrated through an in-depth theoretical
study which includes generalisation bounds and proofs of frequentist
consistency and robustness of our posterior. The approach is then assessed on a
range of examples including a g-and-k distribution and a toggle-switch model.

24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
19
The categories of this paper are: eess