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The following vision paper is currently the main paper for Miking. Please cite this paper if you are using the Miking framework or if you would like to refer to it.

  • David Broman. A Vision of Miking: Interactive Programmatic Modeling, Sound Language Composition, and Self-Learning Compilation. In Proceedings of the 12th ACM SIGPLAN International Conference on Software Language Engineering (SLE 2019), Athens, Greece, ACM, 2019. [PDF] [ACM Link]

The following publications are related to the Miking effort, directly or indirectly:

  • Daniel Lundén, Lars Hummelgren, Jan Kudlicka, Oscar Eriksson, and David Broman. Suspension Analysis and Selective Continuation-Passing Style for Universal Probabilistic Programming Languages. In Proceedings of the 33nd European Symposium on Programming (ESOP 2024), 2024. (to appear)

  • Oscar Eriksson, Viktor Palmkvist, and David Broman. Partial Evaluation of Automatic Differentiation for Differential-Algebraic Equations Solvers. In Proceedings of the 22nd ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences (GPCE 2023), 2023. [Open Access, Springer Link]

  • Daniel Lundén, Gizem Çaylak, Fredrik Ronquist, and David Broman. Automatic Alignment in Higher-Order Probabilistic Programming Languages. In Proceedings of the 32nd European Symposium on Programming (ESOP 2023), 2023. (Best Paper Award) [Open Access, Springer Link]

  • Viktor Palmkvist, Elias Castegren, Philipp Haller, and David Broman. Statically Resolvable Ambiguity. In Proceedings of the 50th ACM SIGPLAN Symposium on Principles of Programming Languages (POPL 2023), 2023. [Open Access, ACM Link]

  • Daniel Lundén, Joey Öhman, Jan Kudlicka, Viktor Senderov, Fredrik Ronquist, and David Broman. Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference. In Proceedings of 31th European Symposium on Programming (ESOP 2022), 2022. [Springer Link] [PDF]

  • Fredrik Ronquist, Jan Kudlicka, Viktor Senderov, Johannes Borgström, Nicolas Lartillot, Daniel Lundén, Lawrence Murray, Thomas B. Schön, and David Broman. Universal probabilistic programming offers a powerful approach to statistical phylogenetics. In Communications Biology, Volume 4, Article number 244, Nature Publishing Group, 2021. [Open Access, Nature Publishing Group]

  • David Broman. Interactive Programmatic Modeling. In ACM Transactions on Embedded Computing Systems (TECS), Volume 20, Issue 4, Article No 33, Pages 1-26, ACM, 2021. [PDF] [ACM Link]

  • Daniel Lundén, Johannes Borgström, and David Broman. Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages. In Proceedings of 30th European Symposium on Programming (ESOP 2021), LNCS vol. 12648, Springer, 2021. [Springer Link] [PDF]

  • Viktor Palmkvist, Elias Castegren, Philipp Haller, and David Broman. Resolvable Ambiguity: Principled Resolution of Syntactically Ambiguous Programs. In Proceedings of the 30th ACM SIGPLAN International Conference on Compiler Construction (CC 2021), ACM 2021. [PDF] [ACM Link] [Artifact]

  • Viktor Palmkvist and David Broman. Creating Domain-Specific Languages by Composing Syntactical Constructs. In Proceedings of the International Symposium on Practical Aspects of Declarative Languages (PADL 2019), Cascais, Portugal, 2019. [PDF] [Springer Link]

The Miking system has been partially inspired by an earlier effort called Modelyze. The following paper outlines the key ideas of Modelyze:

  • David Broman and Jeremy G. Siek. Gradually Typed Symbolic Expressions. In Proceedings of the ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation (PEPM 2018), Los Angeles, ACM, 2018. [PDF] [ACM Link]