Distribution Search FAQs

Question 1

What optimization techniques are included?

Accepted Answer

The skill employs a multi-phase approach including analytical derivations, root-finding (fsolve), least-squares minimization, and grid search for discrete parameters. This variety ensures that Claude can find solutions even in complex or non-convex constraint landscapes.

Question 2

When should I use this skill in Claude Code?

Accepted Answer

Use this skill when you need to construct discrete or continuous probability distributions with specific properties. It is particularly useful for tasks involving information projection, vocabulary weighting, machine learning loss analysis, or any scenario requiring numerical search over distribution parameters.

Question 3

How does this skill ensure the accuracy of the resulting distribution?

Accepted Answer

It implements a rigorous verification phase that checks for fundamental probability properties (positivity, sum-to-one) and validates that the final distribution meets the specific target constraints within defined numerical tolerances (e.g., 1e-6).

Question 4

What does the Distribution Search skill do?

Accepted Answer

This skill provides a systematic framework for Claude to find probability distributions that satisfy complex statistical constraints. It uses mathematical analysis, efficient parameterization, and numerical optimization to meet targets for KL divergence, entropy, and moment conditions.

Question 5

How does it handle large-scale data or large vocabularies?

Accepted Answer

The skill avoids memory-intensive array creation by using closed-form analytical formulas and efficient parameterization strategies, such as two-group distributions. This allows Claude to solve optimization problems for large vocabularies (e.g., 150,000+ elements) without performance degradation.

Distribution Search

Distribution Search

主な機能

ユースケース

主な機能

ユースケース