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TensorFlow Distributions and composable bijectors

Contribution grade

Differentiable probabilistic computing · Human-designed research method or resource

Reference score130,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Separates probability-distribution objects from deterministic volume-tracking transformations, with tensor batch/event semantics, differentiable operations and composable transformed distributions.

Scientific scope

Separates probability-distribution objects from deterministic volume-tracking transformations, with tensor batch/event semantics, differentiable operations and composable transformed distributions. This original library explicitly excludes arbitrary distributional calculus and approximate-inference engines from its core contract. Analytical density/Jacobian requirements restrict supported families; API design goals are not independently verified numerical guarantees for every input. Stars denote Google affiliation, not equal contribution.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. This original library explicitly excludes arbitrary distributional calculus and approximate-inference engines from its core contract. Analytical density/Jacobian requirements restrict supported families; API design goals are not independently verified numerical guarantees for every input. Stars denote Google affiliation, not equal contribution.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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