=Paper=
{{Paper
|id=Vol-1916/intro2
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1916/invited2.pdf
|volume=Vol-1916
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1916/invited2.pdf</pdf>
<pre>
 PRISM Revisited: Declarative Implementation
of a Probabilistic Programming Language Using
                Delimited Control

                                Samer Abdallah

                                  Jukedeck Ltd

    PRISM is a probabilistic programming language based on Prolog augmented
with primitives to represent probabilistic choice. PRISM is implemented using
a combination of low level support from a modified version of B-Prolog, source
level program transformation, and libraries for probabilistic inference and learn-
ing implemented in the imperative language C. More recently, developers of
probabilistic languages working in the functional programming paradigm have
taken the approach of embedding probabilistic primitives into an existing lan-
guage, with little or no modification to the host language, primarily by using
continuations: captured continuations represent pieces of the probabilistic pro-
gram which can be manipulated to achieve a great variety of computational
effects.
    In this talk, I will describe an approach based on delimited control operators
recently introduced into SWI Prolog. These are used to create a system of nested
effect handlers which together implement a core functionality of PRISM—the
building of explanation graphs—entirely in Prolog and using an order of mag-
nitude less code. In addition, other declarative programming tools, such as con-
straint logic programming, are used to implement several tools for inference, such
as the inside-outside and EM algorithms, lazy best-first explanation search, and
MCMC samplers.
    By embedding the functionality of PRISM into SWI Prolog, users gain access
to its rich libraries and development environment. By expressing the functional-
ity of PRISM in a relatively small amount of pure, high-level Prolog, this imple-
mentation will hopefully facilitate further experimentation with the mechanisms
of probabilistic logic programming and extensions to new modelling features.




                                        2

</pre>