Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Particle Gibbs with Ancestor Sampling for Probabilistic Programs   Particle Markov chain Monte Carlo techniques rank among current
state-of-the-art methods for probabilistic program inference. A drawback of
these techniques is that they rely on importance resampling, which results in
degenerate particle trajectories and a low effective sample size for variables
sampled early in a program. We here develop a formalism to adapt ancestor
resampling, a technique that mitigates particle degeneracy, to the
probabilistic programming setting. We present empirical results that
demonstrate nontrivial performance gains.

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The categories of this paper are: plasm-ph
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The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-ex
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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: chem-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: mtrl-th