Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Minimum Message Length Clustering Using Gibbs Sampling   The K-Mean and EM algorithms are popular in clustering and mixture modeling,
due to their simplicity and ease of implementation. However, they have several
significant limitations. Both coverage to a local optimum of their respective
objective functions (ignoring the uncertainty in the model space), require the
apriori specification of the number of classes/clsuters, and are inconsistent.
In this work we overcome these limitations by using the Minimum Message Length
(MML) principle and a variation to the K-Means/EM observation assignment and
parameter calculation scheme. We maintain the simplicity of these approaches
while constructing a Bayesian mixture modeling tool that samples/searches the
model space using a Markov Chain Monte Carlo (MCMC) sampler known as a Gibbs
sampler. Gibbs sampling allows us to visit each model according to its
posterior probability. Therefore, if the model space is multi-modal we will
visit all models and not get stuck in local optima. We call our approach
multiple chains at equilibrium (MCE) MML sampling.

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The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
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The categories of this paper are: nlin
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: nucl-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat
3
The categories of this paper are: physics