What is recorded here?
Presents a Python model-context interface using symbolic computation for Bayesian model specification and gradient-based HMC/NUTS sampling.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The report concerns PyMC3 rather than the latest PyMC release. NUTS, Hamiltonian Monte Carlo and symbolic automatic differentiation are inherited methods. A convenient model specification does not establish convergence or posterior validity.
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People and systems
John Salvatier; Thomas V. Wiecki; Christopher Fonnesbeck
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
PyMC3 Python probabilistic modeling and gradient-based sampling
Presents a Python model-context interface using symbolic computation for Bayesian model specification and gradient-based HMC/NUTS sampling.
Scientific scope
The report concerns PyMC3 rather than the latest PyMC release. NUTS, Hamiltonian Monte Carlo and symbolic automatic differentiation are inherited methods. A convenient model specification does not establish convergence or posterior validity.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
John Salvatier; Thomas V. Wiecki; Christopher Fonnesbeck
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.