Probabilistic Programming
90 papers with code • 0 benchmarks • 0 datasets
Probabilistic programming languages are designed to describe probabilistic models and then perform inference in those models. PPLs are closely related to graphical models and Bayesian networks, but are more expressive and flexible.
( Image credit: Michael Betancourt )
Benchmarks
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Libraries
Use these libraries to find Probabilistic Programming models and implementationsMost implemented papers
TensorFlow Distributions
The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation.
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
pyro-ppl/funsor
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For inference with Sequential Monte Carlo, this automatically yields improvements such as locally-optimal proposals and Rao-Blackwellization.
Automatic Differentiation Variational Inference
Probabilistic modeling is iterative.
Inference Compilation and Universal Probabilistic Programming
pyprob/pyprob
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We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods.
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
pyprob/pyprob
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NeurIPS 2019
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.
An Introduction to Probabilistic Programming
We start with a discussion of model-based reasoning and explain why conditioning is a foundational computation central to the fields of probabilistic machine learning and artificial intelligence.
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
pyprob/pyprob
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Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models.
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro
pyro-ppl/numpyro
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NumPyro is a lightweight library that provides an alternate NumPy backend to the Pyro probabilistic programming language with the same modeling interface, language primitives and effect handling abstractions.
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison
alessiospuriomancini/cosmopower
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First, we demonstrate the application of this paradigm on a simulated cosmic shear analysis for a Stage IV survey in 37- and 39-dimensional parameter spaces, comparing $\Lambda$CDM and a dynamical dark energy model ($w_0w_a$CDM).
Scenic: A Language for Scenario Specification and Scene Generation
We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning.
