Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bambi: A simple interface for fitting Bayesian linear models in Python   The popularity of Bayesian statistical methods has increased dramatically in
recent years across many research areas and industrial applications. This is
the result of a variety of methodological advances with faster and cheaper
hardware as well as the development of new software tools. Here we introduce an
open source Python package named Bambi (BAyesian Model Building Interface) that
is built on top of the PyMC probabilistic programming framework and the ArviZ
package for exploratory analysis of Bayesian models. Bambi makes it easy to
specify complex generalized linear hierarchical models using a formula notation
similar to those found in R. We demonstrate Bambi's versatility and ease of use
with a few examples spanning a range of common statistical models including
multiple regression, logistic regression, and mixed-effects modeling with
crossed group specific effects. Additionally we discuss how automatic priors
are constructed. Finally, we conclude with a discussion of our plans for the
future development of Bambi.

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The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
3
The categories of this paper are: physics
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The categories of this paper are: supr-con
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ph
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The categories of this paper are: cs
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The categories of this paper are: q-bio
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The categories of this paper are: nlin