Probabilistic programming

By A. Gordon, Thomas A. Henzinger, Aditya V. Nori, Sriram K. Rajamani. 2014

774 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Software, Probabilistic & Bayesian Methods

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Citations in the corpus

A Survey of Machine Learning for Big Code and Naturalness (2017, background): “earch in malware detection is related to probabilistic models of code [18, 36]. Another active area at the intersection machine learning and programming language research is probabilistic programming [73]. This might appear to be related to statistical models of code, but in fact there is a fundamen”
Foundation Posteriors for Approximate Probabilistic Inference (2022, neutral): “The primary goal of probabilistic programming is to enable practitioners from any domain to easily reason about random variables of interest [29, 61].”
The frontier of simulation-based inference (2019, background): “(40) describe probabilistic programs as the usual functional or imperative programs with two added constructs: 1) the ability to draw values at random from distributions and 2) the ability to condition values of variables in a program via observations.”
Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, background): “In probabilistic programming (PP), instead of representing probabilistic models by graphical models or Bayesian networks, one uses programs to represent the models which are more expressive and flexible (Gordon et al. 2014).”
DeepProbLog: Neural Probabilistic Logic Programming (2018, background): “The coin-ball problem is a standard example in the probabilistic programming community [De Raedt and Kimmig, 2014].”
Computing functions of random variables via reproducing kernel Hilbert space representations (2015, neutral): “The latter is the main topic of a subject area that has recently begun to attract attention, probabilistic programming (Gordon et al. 2014).”
Automatic differentiation in machine learning: a survey (2015, background): “, 2016) work by training neural networks for performing approximate inference in generative models defined as probabilistic programs (Gordon et al., 2014).”
Inference Compilation and Universal Probabilistic Programming (2016, background): “Probabilistic programs denote probabilistic generative models as programs that include sample and observe statements (Gordon et al., 2014).”
A lambda-calculus foundation for universal probabilistic programming (2015, background): “This has been a source of inspiration for AI researchers, and has recently been gathering interest in the programming language community (see Goodman (2013), Gordon et al. (2014), and Russell (2015)).”
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model (2018, methodology): “In this work, we take this approach, extend previous work in universal probabilistic programming [15] and inference compilation [16] to large-scale complex simulators, and demonstrate the ability to execute existing simulator codes under the control of general-purpose inference engines.”
Introduction to MAchine Learning & Knowledge Extraction (MAKE) (2017, background): “The two additional powerful constructs to functional or imperative programming concepts include [17]:”
Variational Inference using Implicit Distributions (2017, background): “For example, the likelihood or forward model might be described as a probabilistic program (Vajda, 2014) whose density is intractable or unknown.”
Reparameterization Gradient for Non-differentiable Models (2018, methodology): “As we illustrate in our experiments section, models satisfying our assumption naturally arise when one starts to use both discrete and continuous random variables or specifies models using programming constructs, such as if statement, as in probabilistic programming [4, 22, 37, 5].”
Scenic: a language for scenario specification and scene generation (2018, methodology): “Complete Scenic code for the bumper-to-bumper scenario as well as other scenarios used as examples in this section or in our experiments, along with images of generated scenes, can be found in Appendix A.”
Nesting Probabilistic Programs (2018, background): “Probabilistic programming systems (PPSs) allow probabilistic models to be represented in the form of a generative model and statements for conditioning on data [7, 9].”
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs (2015, methodology): “Our primary interest in this work is to improve MCMC methods for probabilistic programming [6,7,11,16].”