Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Frequentism and Bayesianism: A Python-driven Primer   This paper presents a brief, semi-technical comparison of the essential
features of the frequentist and Bayesian approaches to statistical inference,
with several illustrative examples implemented in Python. The differences
between frequentism and Bayesianism fundamentally stem from differing
definitions of probability, a philosophical divide which leads to distinct
approaches to the solution of statistical problems as well as contrasting ways
of asking and answering questions about unknown parameters. After an
example-driven discussion of these differences, we briefly compare several
leading Python statistical packages which implement frequentist inference using
classical methods and Bayesian inference using Markov Chain Monte Carlo.

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The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: supr-con
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The categories of this paper are: alg-geom
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
34
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: q-fin
32
The categories of this paper are: atom-ph