monte-carlo-simulation

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Monte Carlo methods for uncertainty quantification

General

What this skill does


# 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

Files: 1

Size: 0.9 KB

Complexity: 8/100

Category: General

Source: https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/science/mathematics/skills/monte-carlo-simulation

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