PyMC3
E435219
PyMC3 is a Python library for probabilistic programming that enables Bayesian statistical modeling and inference using advanced Markov chain Monte Carlo and variational methods.
All labels observed (3)
| Label | Occurrences |
|---|---|
| PyMC | 4 |
| PyMC (v4+) | 1 |
| PyMC3 canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4390986 — 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: PyMC3 Context triple: [Theano, usedAsBackendFor, PyMC3]
-
A.
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.
-
B.
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.
-
C.
Bayesian linear regression
Bayesian linear regression is a statistical modeling approach that treats regression coefficients and predictions probabilistically by placing prior distributions on parameters and updating them with observed data.
-
D.
Gibbs sampling
Gibbs sampling is a Markov chain Monte Carlo algorithm that generates samples from complex multivariate probability distributions by iteratively sampling each variable from its conditional distribution given the others.
-
E.
Bayesian inference
Bayesian inference is a statistical framework that updates the probability of hypotheses as more evidence or data becomes available, using Bayes’ theorem to combine prior beliefs with observed information.
- 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: PyMC3 Target entity description: PyMC3 is a Python library for probabilistic programming that enables Bayesian statistical modeling and inference using advanced Markov chain Monte Carlo and variational methods.
-
A.
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.
-
B.
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.
-
C.
Bayesian linear regression
Bayesian linear regression is a statistical modeling approach that treats regression coefficients and predictions probabilistically by placing prior distributions on parameters and updating them with observed data.
-
D.
Gibbs sampling
Gibbs sampling is a Markov chain Monte Carlo algorithm that generates samples from complex multivariate probability distributions by iteratively sampling each variable from its conditional distribution given the others.
-
E.
Bayesian inference
Bayesian inference is a statistical framework that updates the probability of hypotheses as more evidence or data becomes available, using Bayes’ theorem to combine prior beliefs with observed information.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
Bayesian modeling framework
ⓘ
Python library ⓘ probabilistic programming library ⓘ |
| domain |
Bayesian inference
ⓘ
machine learning ⓘ statistical modeling ⓘ |
| hasFeature |
ArviZ integration
ⓘ
Gaussian process models ⓘ automatic differentiation ⓘ custom probability distributions ⓘ diagnostic plots ⓘ gradient-based sampling ⓘ hierarchical models ⓘ model comparison tools ⓘ model specification in Python code ⓘ posterior predictive checks ⓘ time series models ⓘ trace plots ⓘ |
| license | Apache License 2.0 ⓘ |
| partOf |
PyMC ecosystem
ⓘ
linked to:
ArviZ
|
| programmingLanguage | Python ⓘ |
| repositoryPlatform | GitHub ⓘ |
| successor |
PyMC (v4+)
ⓘ
linked to:
PyMC3
|
| supportsComputation | CPU ⓘ |
| supportsMethod |
Hamiltonian Monte Carlo
ⓘ
Markov chain Monte Carlo ⓘ No-U-Turn Sampler ⓘ automatic differentiation variational inference ⓘ maximum a posteriori estimation ⓘ variational inference ⓘ |
| supportsModelType |
Bayesian regression
ⓘ
Gaussian process regression ⓘ hierarchical Bayesian models ⓘ mixture models ⓘ state-space models ⓘ |
| supportsOS |
Linux
ⓘ
Windows ⓘ macOS ⓘ |
| supportsParadigm |
Bayesian statistics
ⓘ
probabilistic programming ⓘ |
| targetUser |
data scientists
ⓘ
researchers ⓘ statisticians ⓘ |
| uses |
Matplotlib
ⓘ
NumPy ⓘ SciPy ⓘ Theano ⓘ |
| writtenIn | Python ⓘ |
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: PyMC3 Description of subject: PyMC3 is a Python library for probabilistic programming that enables Bayesian statistical modeling and inference using advanced Markov chain Monte Carlo and variational methods.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: PyMC3
linked to: PyMC3
linked to: PyMC3