Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CrossCat: A Fully Bayesian Nonparametric Method for Analyzing
  Heterogeneous, High Dimensional Data   There is a widespread need for statistical methods that can analyze
high-dimensional datasets with- out imposing restrictive or opaque modeling
assumptions. This paper describes a domain-general data analysis method called
CrossCat. CrossCat infers multiple non-overlapping views of the data, each
consisting of a subset of the variables, and uses a separate nonparametric
mixture to model each view. CrossCat is based on approximately Bayesian
inference in a hierarchical, nonparamet- ric model for data tables. This model
consists of a Dirichlet process mixture over the columns of a data table in
which each mixture component is itself an independent Dirichlet process mixture
over the rows; the inner mixture components are simple parametric models whose
form depends on the types of data in the table. CrossCat combines strengths of
mixture modeling and Bayesian net- work structure learning. Like mixture
modeling, CrossCat can model a broad class of distributions by positing latent
variables, and produces representations that can be efficiently conditioned and
sampled from for prediction. Like Bayesian networks, CrossCat represents the
dependencies and independencies between variables, and thus remains accurate
when there are multiple statistical signals. Inference is done via a scalable
Gibbs sampling scheme; this paper shows that it works well in practice. This
paper also includes empirical results on heterogeneous tabular data of up to 10
million cells, such as hospital cost and quality measures, voting records,
unemployment rates, gene expression measurements, and images of handwritten
digits. CrossCat infers structure that is consistent with accepted findings and
common-sense knowledge in multiple domains and yields predictive accuracy
competitive with generative, discriminative, and model-free alternatives.

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The categories of this paper are: funct-an
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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
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The categories of this paper are: q-bio
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-th
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The categories of this paper are: math