Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The $\theta$-augmented model for Bayesian semiparametric inference on
  functional parameters   Semiparametric Bayesian inference has so far relied on models for the
observable that partition into two parts, one being parametric and the other
nonparametric, with the target parameter being dependent on the parametric
component. While a partitioned structure makes specification of the marginal
prior on the target parameter simple to perform, it often arises from
conditional modelling which is subject to misspecification and ultimately a
lack of consistency. We introduce a new type of semiparametric model to allow
easy prior specification for a parameter that is defined as a functional of the
distribution for the observable. Our semiparametric model is obtained as an
extension of nonparametric models that are consistent under very general
conditions. This type of Bayesian semiparametric model can be used to obtain
Bayesian versions of Frequentist estimators that are defined as functionals of
the empirical distribution. This gives us new opportunities to conduct Bayesian
analysis in problems where Frequentist estimators exist but not well-accepted
likelihoods.

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The categories of this paper are: hep-ph
1
The categories of this paper are: cs
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess