Distribution Search

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Streamlines the process of constructing high-dimensional probability distributions that meet precise numerical properties such as KL divergence, entropy, and other information-theoretic metrics. By prioritizing mathematical analysis before implementation, the skill guides users through choosing efficient parameterization strategies—like power law or multi-tier models—to navigate large vocabulary spaces. It provides a robust framework for modular implementation, optimization refinement, and independent verification, ensuring solutions are both statistically accurate and computationally feasible.

Características Principales

• Mathematical analysis of target metrics and analytical relationships
• Guided optimization strategies including grid search and local refinement
• Independent verification workflows to prevent common numerical bugs
• Modular implementation patterns for efficient computation and testing
• Reduced parameterization strategies for high-dimensional optimization
• 16 GitHub stars

Casos de Uso

• Optimizing probability distributions over large vocabularies for AI benchmarking
• Constructing synthetic datasets with specific entropy or KL divergence profiles
• Satisfying simultaneous statistical constraints in information-theoretic modeling