Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Relabelling Algorithms for Large Dataset Mixture Models   Mixture models are flexible tools in density estimation and classification
problems. Bayesian estimation of such models typically relies on sampling from
the posterior distribution using Markov chain Monte Carlo. Label switching
arises because the posterior is invariant to permutations of the component
parameters. Methods for dealing with label switching have been studied fairly
extensively in the literature, with the most popular approaches being those
based on loss functions. However, many of these algorithms turn out to be too
slow in practice, and can be infeasible as the size and dimension of the data
grow. In this article, we review earlier solutions which can scale up well for
large data sets, and compare their performances on simulated and real datasets.
In addition, we propose a new, and computationally efficient algorithm based on
a loss function interpretation, and show that it can scale up well in larger
problems. We conclude with some discussions and recommendations of all the
methods studied.

14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
1
The categories of this paper are: cs
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin