Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deriving Probability Density Functions from Probabilistic Functional
  Programs   The probability density function of a probability distribution is a
fundamental concept in probability theory and a key ingredient in various
widely used machine learning methods. However, the necessary framework for
compiling probabilistic functional programs to density functions has only
recently been developed. In this work, we present a density compiler for a
probabilistic language with failure and both discrete and continuous
distributions, and provide a proof of its soundness. The compiler greatly
reduces the development effort of domain experts, which we demonstrate by
solving inference problems from various scientific applications, such as
modelling the global carbon cycle, using a standard Markov chain Monte Carlo
framework.

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The categories of this paper are: q-alg
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
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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: nucl-ex
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The categories of this paper are: econ