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10.21105.joss.00011.jats
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<?xml version="1.0" encoding="utf-8" ?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN"
"JATS-publishing1.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="1.2" article-type="other">
<front>
<journal-meta>
<journal-id></journal-id>
<journal-title-group>
<journal-title>Journal of Open Source Software</journal-title>
<abbrev-journal-title>JOSS</abbrev-journal-title>
</journal-title-group>
<issn publication-format="electronic">2475-9066</issn>
<publisher>
<publisher-name>Open Journals</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">11</article-id>
<article-id pub-id-type="doi">10.21105/joss.00011</article-id>
<title-group>
<article-title>carl: a likelihood-free inference toolbox</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">0000-0002-2082-3106</contrib-id>
<string-name>Gilles Louppe</string-name>
<xref ref-type="aff" rid="aff-1"/>
</contrib>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">0000-0002-5769-7094</contrib-id>
<string-name>Kyle Cranmer</string-name>
<xref ref-type="aff" rid="aff-1"/>
</contrib>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">0000-0002-7205-0053</contrib-id>
<string-name>Juan Pavez</string-name>
<xref ref-type="aff" rid="aff-2"/>
</contrib>
<aff id="aff-1">
<institution-wrap>
<institution>New York University</institution>
</institution-wrap>
</aff>
<aff id="aff-2">
<institution-wrap>
<institution>Federico Santa María University</institution>
</institution-wrap>
</aff>
</contrib-group>
<pub-date date-type="pub" publication-format="electronic" iso-8601-date="2016-05-04">
<day>4</day>
<month>5</month>
<year>2016</year>
</pub-date>
<volume>1</volume>
<issue>1</issue>
<fpage>11</fpage>
<permissions>
<copyright-statement>Authors of papers retain copyright and release the
work under a Creative Commons Attribution 4.0 International License (CC
BY 4.0)</copyright-statement>
<copyright-year>2021</copyright-year>
<copyright-holder>The article authors</copyright-holder>
<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
<license-p>Authors of papers retain copyright and release the work under
a Creative Commons Attribution 4.0 International License (CC BY
4.0)</license-p>
</license>
</permissions>
<kwd-group kwd-group-type="author">
<kwd>likehood-free inference</kwd>
<kwd>density ratio estimation</kwd>
</kwd-group>
</article-meta>
</front>
<body>
<sec id="summary">
<title>Summary</title>
<p>Carl is a toolbox for likelihood-free inference in Python.</p>
<p>The likelihood function is the central object that summarizes the
information from an experiment needed for inference of model
parameters. It is key to many areas of science that report the results
of classical hypothesis tests or confidence intervals using the
(generalized or profile) likelihood ratio as a test statistic. At the
same time, with the advance of computing technology, it has become
increasingly common that a simulator (or generative model) is used to
describe complex processes that tie parameters of an underlying theory
and measurement apparatus to high-dimensional observations. However,
directly evaluating the likelihood function in these cases is often
impossible or is computationally impractical.</p>
<p>In this context, the goal of this package is to provide tools for
the likelihood-free setup, including likelihood (or density) ratio
estimation algorithms, along with helpers to carry out inference on
top of these.</p>
<sec id="approximating-likelihood-ratios-with-calibrated-classifiers">
<title>Approximating likelihood ratios with calibrated
classifiers</title>
<p>Methodological details regarding likelihood-free inference with
calibrated classifiers can be found in the companion paper
(<xref alt="Cranmer et al., 2016" rid="ref-CranmerU003A2015-llr" ref-type="bibr">Cranmer
et al., 2016</xref>).</p>
</sec>
<sec id="future-works">
<title>Future works</title>
<p>Future development aims at providing further density ratio
estimation algorithms, along with alternative algorithms for the
likelihood-free setup, such as Approximate Bayesian Computation
(ABC).</p>
</sec>
</sec>
</body>
<back>
<ref-list>
<ref-list>
<ref id="ref-CranmerU003A2015-llr">
<element-citation publication-type="article-journal">
<person-group person-group-type="author">
<name><surname>Cranmer</surname><given-names>Kyle</given-names></name>
<name><surname>Pavez</surname><given-names>Juan</given-names></name>
<name><surname>Louppe</surname><given-names>Gilles</given-names></name>
</person-group>
<article-title>Approximating Likelihood Ratios with Calibrated Discriminative Classifiers</article-title>
<year iso-8601-date="2016-03-18">2016</year><month>03</month><day>18</day>
<uri>http://arxiv.org/abs/1506.02169v2</uri>
</element-citation>
</ref>
</ref-list>
</ref-list>
</back>
</article>