Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Approximate Inference for Stochastic Planning in Factored Spaces   The paper explores the use of approximate inference techniques as solution
methods for stochastic planning problems with discrete factored spaces. While
much prior work exists on this topic, subtle variations hinder a global
understanding of different approaches for their differences and potential
advantages. Here we abstract a simple framework that captures and connects
prior work along two dimensions, direction of information flow, i.e., forward
vs backward inference, and the type of approximation used, e.g., Belief
Propagation (BP) vs mean field variational inference (MFVI). Through this
analysis we also propose a novel algorithm, CSVI, which provides a tighter
variational approximation compared to prior work. An extensive experimental
evaluation compares algorithms from different branches of the framework,
showing that methods based on BP are generally better than methods based on
MFVI, that CSVI is competitive with BP algorithms, and that while inference
direction does not show a significant effect for VI methods, forward inference
provides stronger performance with BP.

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The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
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The categories of this paper are: math-ph
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
2
The categories of this paper are: math
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The categories of this paper are: q-fin
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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: quant-ph