monte-carlo-simulation

name	monte-carlo-simulation
description	Monte Carlo methods for uncertainty quantification
allowed-tools	["Bash","Read","Write","Edit","Glob","Grep"]
metadata	{"specialization":"mathematics","domain":"science","category":"uncertainty-quantification","phase":6}

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