Probabilistic Programming
87 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 )
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Libraries
Use these libraries to find Probabilistic Programming models and implementationsMost implemented papers
Better call Saul: Flexible Programming for Learning and Inference in NLP
We present a novel way for designing complex joint inference and learning models using Saul (Kordjamshidi et al., 2015), a recently-introduced declarative learning-based programming language (DeLBP).
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes
We found that human evaluators often prefer the results from probabilistic search to results from a standard baseline.
ZhuSuan: A Library for Bayesian Deep Learning
thu-ml/zhusuan
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In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning.
Bayesian Neural Networks
Thus BNNs are a unique combination of neural network and stochastic models with the stochastic model forming the core of this integration.
Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities
Hamiltonian Monte Carlo (HMC) is arguably the dominant statistical inference algorithm used in most popular "first-order differentiable" Probabilistic Programming Languages (PPLs).
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming
We design a new myopic strategy for a wide class of sequential design of experiment (DOE) problems, where the goal is to collect data in order to to fulfil a certain problem specific goal.
Machine Teaching of Active Sequential Learners
AaltoPML/machine-teaching-of-active-sequential-learners
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NeurIPS 2019
We formulate this sequential teaching problem, which current techniques in machine teaching do not address, as a Markov decision process, with the dynamics nesting a model of the learner and the actions being the teacher's responses.
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming
deepppl/deepppl
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We use our compilation scheme to build two new backends for the Stanc3 compiler targeting Pyro and NumPyro.
Pyro: Deep Universal Probabilistic Programming
uber/pyro
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Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research.
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic
Stan is a probabilistic programming language that has been increasingly used for real-world scalable projects.
