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BibTeX entry
@Article{Cassel:2014:PPS,
author = "John Cassel",
title = "Probabilistic Programming with Stochastic Memoization:
Exploring Fractal Curves Implementing Nonparametric
{Bayesian} Inference",
journal = j-MATHEMATICA-J,
volume = "16",
number = "??",
pages = "??--??",
month = "????",
year = "2014",
CODEN = "????",
ISSN = "1047-5974 (print), 1097-1610 (electronic)",
bibdate = "Sat Mar 15 08:18:52 MDT 2014",
bibsource = "http://www.math.utah.edu/pub/tex/bib/mathematicaj.bib",
URL = "http://www.mathematica-journal.com/2014/01/probabilistic-programming-with-stochastic-memoization/",
abstract = "Probabilistic programming is a programming language
paradigm receiving both government support [ 1 ] and
the attention of the popular technology press [ 2 ].
Probabilistic programming concerns writing programs
with segments that can be interpreted as parameter and
conditional distributions, yielding statistical
findings through nonstandard execution. Mathematica not
only has great support for statistics, but has another
language feature particular to probabilistic language
elements, namely memoization, which is the ability for
functions to retain their value for particular function
calls across parameters, creating random trials that
retain their value. Recent research has found that
reasoning about processes instead of given parameters
has allowed Bayesian inference to undertake more
flexible models that require computational support.
This article explains this nonparametric Bayesian
inference, shows how Mathematica's capacity for
memoization supports probabilistic programming
features, and demonstrates this capability through two
examples, learning systems of relations and learning
arithmetic functions based on output.",
acknowledgement = ack-nhfb,
journal-URL = "http://www.mathematica-journal.com/",
}
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