@article {Teugels90,
    AUTHOR = {Teugels, Jozef L.},
     TITLE = {Some representations of the multivariate {B}ernoulli and
              binomial distributions},
   JOURNAL = {J. Multivariate Anal.},
  FJOURNAL = {Journal of Multivariate Analysis},
    VOLUME = {32},
      YEAR = {1990},
    NUMBER = {2},
     PAGES = {256--268},
      ISSN = {0047-259X,1095-7243},
   MRCLASS = {62H17 (62H20)},
  MRNUMBER = {1046768},
MRREVIEWER = {Friedrich\ Liese},
       DOI = {10.1016/0047-259X(90)90084-U},
       URL = {https://doi.org/10.1016/0047-259X(90)90084-U},
}


@book{kindermann80,
  author     = {Kindermann, Ross and Snell, J. Laurie},
  title      = {Markov random fields and their applications},
  series     = {Contemporary Mathematics},
  volume     = {1},
  publisher  = {American Mathematical Society, Providence, RI},
  year       = {1980},
  pages      = {ix+142},
  isbn       = {0-8218-5001-6},
  mrclass    = {60K35 (60G60 82A42 82A67 94A05)},
  mrnumber   = {620955},
  mrreviewer = {J.\ Theodore\ Cox}
}

@article{geman84,
  author  = {Geman, Stuart and Geman, Donald},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title   = {Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images},
  year    = {1984},
  volume  = {PAMI-6},
  number  = {6},
  pages   = {721-741},
  doi     = {10.1109/TPAMI.1984.4767596}
}


@book{Judea88,
  author     = {Pearl, Judea},
  title      = {Probabilistic reasoning in intelligent systems: networks of
                plausible inference},
  series     = {The Morgan Kaufmann Series in Representation and Reasoning},
  publisher  = {Morgan Kaufmann, San Mateo, CA},
  year       = {1988},
  pages      = {xx+552},
  isbn       = {0-934613-73-7},
  mrclass    = {68-02 (68T01 92A25)},
  mrnumber   = {965765},
  mrreviewer = {V. Yu. Trakhtman}
}

 @article{sympy,
  title    = {SymPy: symbolic computing in Python},
  author   = {Meurer, Aaron and Smith, Christopher P. and Paprocki, Mateusz and \v{C}ert\'{i}k, Ond\v{r}ej and Kirpichev, Sergey B. and Rocklin, Matthew and Kumar, AMiT and Ivanov, Sergiu and Moore, Jason K. and Singh, Sartaj and Rathnayake, Thilina and Vig, Sean and Granger, Brian E. and Muller, Richard P. and Bonazzi, Francesco and Gupta, Harsh and Vats, Shivam and Johansson, Fredrik and Pedregosa, Fabian and Curry, Matthew J. and Terrel, Andy R. and Rou\v{c}ka, \v{S}t\v{e}p\'{a}n and Saboo, Ashutosh and Fernando, Isuru and Kulal, Sumith and Cimrman, Robert and Scopatz, Anthony},
  year     = 2017,
  month    = jan,
  keywords = {Python, Computer algebra system, Symbolics},
  abstract = {
              SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.
              },
  volume   = 3,
  pages    = {e103},
  journal  = {PeerJ Computer Science},
  issn     = {2376-5992},
  url      = {https://doi.org/10.7717/peerj-cs.103},
  doi      = {10.7717/peerj-cs.103}
}

@inproceedings{verreet2022inference,
  title     = {Inference and learning with model uncertainty in probabilistic logic programs},
  author    = {Verreet, Victor and Derkinderen, Vincent and Dos Martires, Pedro Zuidberg and De Raedt, Luc},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume    = {36},
  number    = {9},
  pages     = {10060--10069},
  year      = {2022}
}

@article{cropper2022inductive,
  title     = {Inductive logic programming at 30},
  author    = {Cropper, Andrew and Duman{\v{c}}i{\'c}, Sebastijan and Evans, Richard and Muggleton, Stephen H},
  journal   = {Machine Learning},
  volume    = {111},
  number    = {1},
  pages     = {147--172},
  year      = {2022},
  publisher = {Springer}
}

@article{cozman2020joy,
  title     = {The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference},
  author    = {Cozman, Fabio Gagliardi and Mau{\'a}, Denis Deratani},
  journal   = {International Journal of Approximate Reasoning},
  volume    = {125},
  pages     = {218--239},
  year      = {2020},
  publisher = {Elsevier}
}

@article{gebser2012answer,
  title     = {Answer set solving in practice},
  author    = {Gebser, Martin and Kaminski, Roland and Kaufmann, Benjamin and Schaub, Torsten},
  journal   = {Synthesis lectures on artificial intelligence and machine learning},
  volume    = {6},
  number    = {3},
  pages     = {1--238},
  year      = {2012},
  publisher = {Morgan \& Claypool Publishers}
}


@article{bezanson2017julia,
  author   = {Bezanson, Jeff and Edelman, Alan and Karpinski, Stefan and Shah, Viral B.},
  title    = {Julia: A Fresh Approach to Numerical Computing},
  journal  = {SIAM Review},
  volume   = {59},
  number   = {1},
  pages    = {65-98},
  year     = {2017},
  doi      = {10.1137/141000671},
  %url      = {https://doi.org/10.1137/141000671},
  %eprint   = {https://doi.org/10.1137/141000671},
  abstract = { Bridging cultures that have often been distant, Julia combines expertise from the diverse fields of computer science and computational science to create a new approach to numerical computing. Julia is designed to be easy and fast and questions notions generally held to be “laws of nature" by practitioners of numerical computing: \beginlist \item High-level dynamic programs have to be slow. \item One must prototype in one language and then rewrite in another language for speed or deployment. \item There are parts of a system appropriate for the programmer, and other parts that are best left untouched as they have been built by the experts. \endlist We introduce the Julia programming language and its design---a dance between specialization and abstraction. Specialization allows for custom treatment. Multiple dispatch, a technique from computer science, picks the right algorithm for the right circumstance. Abstraction, which is what good computation is really about, recognizes what remains the same after differences are stripped away. Abstractions in mathematics are captured as code through another technique from computer science, generic programming. Julia shows that one can achieve machine performance without sacrificing human convenience. }
}

@article{gowda2021high,
  title={High-performance symbolic-numerics via multiple dispatch},
  author={Gowda, Shashi and Ma, Yingbo and Cheli, Alessandro and Gwozdz, Maja and Shah, Viral B and Edelman, Alan and Rackauckas, Christopher},
  journal={arXiv preprint arXiv:2105.03949},
  year={2021}
}

@article{bouchetvalat2023dataframes,
 title={DataFrames.jl: Flexible and Fast Tabular Data in Julia},
 volume={107},
 %url={https://www.jstatsoft.org/index.php/jss/article/view/v107i04},
 doi={10.18637/jss.v107.i04},
 abstract={DataFrames.jl is a package written for and in the Julia language offering flexible and efficient handling of tabular data sets in memory. Thanks to Julia’s unique strengths, it provides an appealing set of features: Rich support for standard data processing tasks and excellent flexibility and efficiency for more advanced and non-standard operations. We present the fundamental design of the package and how it compares with implementations of data frames in other languages, its main features, performance, and possible extensions. We conclude with a practical illustration of typical data processing operations.},
 number={4},
 journal={Journal of Statistical Software},
 author={Bouchet-Valat, Milan and Kamiński, Bogumił},
 year={2023},
 pages={1--32}
}

@book{riguzzi2022foundations,
	address = {New York},
	edition = {1},
	title = {Foundations of {Probabilistic} {Logic} {Programming}: {Languages}, {Semantics}, {Inference} and {Learning}},
	isbn = {978-1-00-333819-2},
	shorttitle = {Foundations of {Probabilistic} {Logic} {Programming}},
	%url = {https://www.taylorfrancis.com/books/9781003338192},
	language = {en},
	urldate = {2023-03-01},
	publisher = {River Publishers},
	author = {Riguzzi, Fabrizio},
	month = sep,
	year = {2022},
	doi = {10.1201/9781003338192},
}

@inproceedings{sato1995statistical,
  title={A Statistical Learning Method for Logic Programs with Distribution Semantics},
  author={Taisuke Sato},
  booktitle={International Conference on Logic Programming},
  year={1995},
  %url={https://api.semanticscholar.org/CorpusID:10424169}
}


@article{lifschitz2002answer,
	title = {Answer set programming and plan generation},
	volume = {138},
	issn = {0004-3702},
	%url = {https://www.sciencedirect.com/science/article/pii/S0004370202001868},
	doi = {https://doi.org/10.1016/S0004-3702(02)00186-8},
	abstract = {The idea of answer set programming is to represent a given computational problem by a logic program whose answer sets correspond to solutions, and then use an answer set solver, such as smodels or dlv, to find an answer set for this program. Applications of this method to planning are related to the line of research on the frame problem that started with the invention of formal nonmonotonic reasoning in 1980.},
	number = {1},
	journal = {Artificial Intelligence},
	author = {Lifschitz, Vladimir},
	year = {2002},
	keywords = {Answer sets, Default logic, Frame problem, Logic programming, Planning},
	pages = {39--54},
}

@inproceedings{lee2016weighted,
  title={Weighted rules under the stable model semantics},
  author={Lee, Joohyung and Wang, Yi},
  booktitle={Fifteenth international conference on the principles of knowledge representation and reasoning},
  year={2016}
}

@article{baral2009probabilistic,
  title={Probabilistic reasoning with {A}nswer {S}ets},
  author={Baral, Chitta and Gelfond, Michael and Rushton, Nelson},
  journal={Theory and Practice of Logic Programming},
  volume={9},
  number={1},
  pages={57--144},
  year={2009},
  publisher={Cambridge University Press}
}

@inproceedings{de2007problog,
  title={ProbLog: A probabilistic {P}rolog and its application in link discovery},
  author={De Raedt, Luc and Kimmig, Angelika and Toivonen, Hannu and Veloso, M},
  booktitle={IJCAI 2007, Proceedings of the 20th international joint conference on artificial intelligence},
  pages={2462--2467},
  year={2007},
  organization={IJCAI-INT JOINT CONF ARTIF INTELL}
}

@inproceedings{lee2017lpmln,
  title={LPMLN, {W}eak {C}onstraints, and {P}-log},
  author={Lee, Joohyung and Yang, Zhun},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={31},
  number={1},
  year={2017}
}

@article{pajunen2021solution,
  title={Solution enumeration by optimality in {A}nswer {S}et {P}rogramming},
  author={Pajunen, Jukka and Janhunen, Tomi},
  journal={Theory and Practice of Logic Programming},
  volume={21},
  number={6},
  pages={750--767},
  year={2021},
  publisher={Cambridge University Press}
}


@article{alberti2017cplint,
  title={cplint on SWISH: Probabilistic logical inference with a web browser},
  author={Alberti, Marco and Bellodi, Elena and Cota, Giuseppe and Riguzzi, Fabrizio and Zese, Riccardo},
  journal={Intelligenza Artificiale},
  volume={11},
  number={1},
  pages={47--64},
  year={2017},
  publisher={IOS Press}
}