Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Arithmetic and Geometric Fusion of Beliefs for Distributed
  Inference   We study the asymptotic learning rates under linear and log-linear
combination rules of belief vectors in a distributed hypothesis testing
problem. We show that under both combination strategies, agents are able to
learn the truth exponentially fast, with a faster rate under log-linear fusion.
We examine the gap between the rates in terms of network connectivity and
information diversity. We also provide closed-form expressions for special
cases involving federated architectures and exchangeable networks.

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The categories of this paper are: chem-ph
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-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: hep-ex
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The categories of this paper are: supr-con
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The categories of this paper are: nlin
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The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
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The categories of this paper are: q-alg
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The categories of this paper are: bayes-an