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Monte carlo simulation

Skill a5c-ai/babysitter/library/specializations/domains/science/mathematics/skills/monte-carlo-simulation

Monte Carlo methods for uncertainty quantificationFrom its SKILL.md

Install
npx -y skills add a5c-ai/babysitter --skill monte-carlo-simulation

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

1.2 KB, 136 tokens by cl100k_base, as published. Nobody here has run it

Monte Carlo Simulation

Purpose

Provides Monte Carlo methods for uncertainty quantification, integration, and probabilistic analysis.

Capabilities

  • Standard Monte Carlo sampling
  • Importance sampling
  • Stratified sampling
  • Quasi-Monte Carlo (Sobol, Halton sequences)
  • Markov chain Monte Carlo
  • Convergence analysis

Usage Guidelines

  1. Sampling Strategy: Choose appropriate sampling method
  2. Sample Size: Determine sufficient sample sizes
  3. Variance Reduction: Apply variance reduction techniques
  4. Convergence: Monitor convergence diagnostics

Tools/Libraries

  • NumPy
  • scipy.stats
  • SALib

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

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