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Babysitter monte-carlo-simulation

Monte Carlo methods for uncertainty quantification

install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/science/mathematics/skills/monte-carlo-simulation" ~/.claude/skills/a5c-ai-babysitter-monte-carlo-simulation && rm -rf "$T"
manifest: library/specializations/domains/science/mathematics/skills/monte-carlo-simulation/SKILL.md
tags
#monte-carlo#uncertainty-quantification#probabilistic-analysis#sampling#variance-reduction
source content

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