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Published equation contexts

logit⁡(pir)=α+ur+β⊤xir,ur∼N(0,σr2)\operatorname{logit}(p_{ir})=\alpha+u_r+\beta^\top x_{ir}, \qquad u_r\sim\mathcal N(0,\sigma_r^2)

Why this formula appears here

A hierarchical model can represent repository effects: logit⁡(pir)=α+ur+β⊤xir,ur∼N(0,σr2)\operatorname{logit}(p_{ir})=\alpha+u_r+\beta^\top x_{ir}, \qquad u_r\sim\mathcal N(0,\sigma_r^2). where r indexes repositories and xirx_{ir} task characteristics. The estimate can then expose between-repository variance rather than hiding it inside a global mean. Bootstrap intervals should resample at relevant task-family or repository levels when generalization across those units is the target.

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pirp_{ir}

Symbol p_ir

pip_ir is an argument of the function-like quantity on the left; its role is set by that function’s stated inputs.

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σr2\sigma_r^2

Symbol sigma_r^2

sigmar2a_r^2 is one of the signed contributions combined to compute the quantity on the left.

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How to interpret it

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Published contexts (1)

A symbol can carry a different meaning in another article. Each occurrence keeps its own guide and term definitions.

logit⁡(pir)=α+ur+β⊤xir,ur∼N(0,σr2),\operatorname{logit}(p_{ir})=\alpha+u_r+\beta^\top x_{ir}, \qquad u_r\sim\mathcal N(0,\sigma_r^2),

Equation 11 · Model Evaluation

How to Evaluate Codex Beyond SWE-bench and Vendor Scores

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

A hierarchical model can represent repository effects: logit⁡(pir)=α+ur+β⊤xir,ur∼N(0,σr2)\operatorname{logit}(p_{ir})=\alpha+u_r+\beta^\top x_{ir}, \qquad u_r\sim\mathcal N(0,\sigma_r^2). where r indexes repositories and xirx_{ir} task characteristics. The estimate can then expose between-repository variance rather than hiding it inside a global mean. Bootstrap intervals should resample at relevant task-family or repository levels when generalization across those units is the target.

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