Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dependent Hierarchical Normalized Random Measures for Dynamic Topic
  Modeling   We develop dependent hierarchical normalized random measures and apply them
to dynamic topic modeling. The dependency arises via superposition, subsampling
and point transition on the underlying Poisson processes of these measures. The
measures used include normalised generalised Gamma processes that demonstrate
power law properties, unlike Dirichlet processes used previously in dynamic
topic modeling. Inference for the model includes adapting a recently developed
slice sampler to directly manipulate the underlying Poisson process.
Experiments performed on news, blogs, academic and Twitter collections
demonstrate the technique gives superior perplexity over a number of previous
models.

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The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
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The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph