NumPyro
E898985
NumPyro is a lightweight probabilistic programming library for Python that leverages JAX to provide high-performance, scalable Bayesian inference with modern MCMC and variational inference algorithms.
All labels observed (1)
| Label | Occurrences |
|---|---|
| NumPyro canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11002333 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: NumPyro Context triple: [Hamiltonian Monte Carlo, implementedIn, NumPyro]
-
A.
PyMC3
PyMC3 is a Python library for probabilistic programming that enables Bayesian statistical modeling and inference using advanced Markov chain Monte Carlo and variational methods.
-
B.
TensorFlow Probability (JAX backend)
TensorFlow Probability (JAX backend) is a probabilistic programming and statistical modeling library that runs on JAX, providing tools for Bayesian inference, probabilistic layers, and advanced distributions with XLA-accelerated computation.
-
C.
Hamiltonian Monte Carlo
Hamiltonian Monte Carlo is an advanced Markov chain Monte Carlo sampling algorithm that uses concepts from Hamiltonian dynamics to efficiently explore complex, high-dimensional probability distributions.
-
D.
Chainer
Chainer is an open-source deep learning framework for Python that pioneered a flexible "define-by-run" computation graph approach to building neural networks.
-
E.
Theano
Theano is an open-source numerical computation library for Python that allows efficient definition, optimization, and evaluation of mathematical expressions, particularly those involving multi-dimensional arrays, and was widely used as a backend for deep learning frameworks.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: NumPyro Target entity description: NumPyro is a lightweight probabilistic programming library for Python that leverages JAX to provide high-performance, scalable Bayesian inference with modern MCMC and variational inference algorithms.
-
A.
PyMC3
PyMC3 is a Python library for probabilistic programming that enables Bayesian statistical modeling and inference using advanced Markov chain Monte Carlo and variational methods.
-
B.
TensorFlow Probability (JAX backend)
TensorFlow Probability (JAX backend) is a probabilistic programming and statistical modeling library that runs on JAX, providing tools for Bayesian inference, probabilistic layers, and advanced distributions with XLA-accelerated computation.
-
C.
Hamiltonian Monte Carlo
Hamiltonian Monte Carlo is an advanced Markov chain Monte Carlo sampling algorithm that uses concepts from Hamiltonian dynamics to efficiently explore complex, high-dimensional probability distributions.
-
D.
Chainer
Chainer is an open-source deep learning framework for Python that pioneered a flexible "define-by-run" computation graph approach to building neural networks.
-
E.
Theano
Theano is an open-source numerical computation library for Python that allows efficient definition, optimization, and evaluation of mathematical expressions, particularly those involving multi-dimensional arrays, and was widely used as a backend for deep learning frameworks.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
Python library
ⓘ
probabilistic programming library ⓘ |
| basedOn | JAX NERFINISHED ⓘ |
| compatibleWith |
JAX NumPy
NERFINISHED
ⓘ
JAX random module ⓘ |
| designedFor |
high-performance Bayesian inference
ⓘ
research in probabilistic programming ⓘ scalable probabilistic modeling ⓘ |
| domain |
Bayesian statistics
ⓘ
machine learning ⓘ probabilistic programming ⓘ |
| hasFeature |
JAX-based random number generation
ⓘ
JIT-compiled log probability evaluation ⓘ NumPy-like modeling syntax ⓘ automatic differentiation variational inference ⓘ diagnostics for MCMC ⓘ integration with JAX transformations ⓘ model transformations ⓘ parallel sampling ⓘ plate notation for independence structure ⓘ reproducible random seeds ⓘ subsampling for large datasets ⓘ support for Bayesian neural networks ⓘ support for custom distributions ⓘ support for custom inference algorithms ⓘ support for discrete and continuous distributions ⓘ support for hierarchical models ⓘ support for probabilistic regression ⓘ support for time series models ⓘ vectorized MCMC chains ⓘ |
| hostedOn | GitHub NERFINISHED ⓘ |
| inspiredBy | Pyro NERFINISHED ⓘ |
| license | Apache License 2.0 ⓘ |
| programmingLanguage | Python ⓘ |
| similarTo |
PyMC
NERFINISHED
ⓘ
Pyro NERFINISHED ⓘ TensorFlow Probability NERFINISHED ⓘ |
| supports |
Bayesian inference
ⓘ
GPU acceleration ⓘ Hamiltonian Monte Carlo NERFINISHED ⓘ Markov chain Monte Carlo NERFINISHED ⓘ No-U-Turn Sampler NERFINISHED ⓘ TPU acceleration ⓘ automatic differentiation ⓘ just-in-time compilation ⓘ stochastic variational inference ⓘ variational inference ⓘ vectorized computation ⓘ |
| uses | XLA compilation via JAX ⓘ |
| writtenIn | Python NERFINISHED ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: NumPyro Description of subject: NumPyro is a lightweight probabilistic programming library for Python that leverages JAX to provide high-performance, scalable Bayesian inference with modern MCMC and variational inference algorithms.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.