Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Median of Means Principle for Bayesian Inference   The topic of robustness is experiencing a resurgence of interest in the
statistical and machine learning communities. In particular, robust algorithms
making use of the so-called median of means estimator were shown to satisfy
strong performance guarantees for many problems, including estimation of the
mean, covariance structure as well as linear regression. In this work, we
propose an extension of the median of means principle to the Bayesian
framework, leading to the notion of the robust posterior distribution. In
particular, we (a) quantify robustness of this posterior to outliers, (b) show
that it satisfies a version of the Bernstein-von Mises theorem that connects
Bayesian credible sets to the traditional confidence intervals, and (c)
demonstrate that our approach performs well in applications.

14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn