Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Density Estimation via Bayesian Inference Engines   We explain how effective automatic probability density function estimates can
be constructed using contemporary Bayesian inference engines such as those
based on no-U-turn sampling and expectation propagation. Extensive simulation
studies demonstrate that the proposed density estimates have excellent
comparative performance and scale well to very large sample sizes due to a
binning strategy. Moreover, the approach is fully Bayesian and all estimates
are accompanied by pointwise credible intervals. An accompanying package in the
R language facilitates easy use of the new density estimates.

12
The categories of this paper are: nucl-th
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The categories of this paper are: econ
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
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The categories of this paper are: eess
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: math
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an