| 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} |
| graph | {"domains":["domain:mathematics"],"specializations":["specialization:computational-mathematics"],"skillAreas":["skill-area:statistical-analysis","skill-area:mathematical-reasoning","skill-area:data-analysis"],"workflows":["workflow:experiment-design"],"roles":["role:research-scientist","role:data-scientist"]} |
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
- Sampling Strategy: Choose appropriate sampling method
- Sample Size: Determine sufficient sample sizes
- Variance Reduction: Apply variance reduction techniques
- Convergence: Monitor convergence diagnostics
Tools/Libraries