Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An adaptive truncation method for inference in Bayesian nonparametric
  models   Many exact Markov chain Monte Carlo algorithms have been developed for
posterior inference in Bayesian nonparametric models which involve
infinite-dimensional priors. However, these methods are not generic and special
methodology must be developed for different classes of prior or different
models. Alternatively, the infinite-dimensional prior can be truncated and
standard Markov chain Monte Carlo methods used for inference. However, the
error in approximating the infinite-dimensional posterior can be hard to
control for many models. This paper describes an adaptive truncation method
which allows the level of the truncation to be decided by the algorithm and so
can avoid large errors in approximating the posterior. A sequence of truncated
priors is constructed which are sampled using Markov chain Monte Carlo methods
embedded in a sequential Monte Carlo algorithm. Implementational details for
infinite mixture models with stick-breaking priors and normalized random
measures with independent increments priors are discussed. The methodology is
illustrated on infinite mixture models, a semiparametric linear mixed model and
a nonparametric time series model.

10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
1
The categories of this paper are: cs
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math