monte-carlo-engine

# Monte Carlo Engine ## Overview The Monte Carlo Engine skill provides comprehensive probabilistic simulation capabilities for quantifying uncertainty, assessing risk, and propagating variability through complex models. It supports multiple sampling strategies, correlation handling, and statistical analysis of simulation outputs for data-driven decision support. ## Capabilities - Random variate generation (normal, triangular, PERT, uniform, lognormal, beta, etc.) - Latin Hypercube Sampling (LHS) - Correlation structure handling (Cholesky decomposition, copulas) - Convergence monitoring and adaptive iteration - Statistical output analysis (mean, variance, percentiles) - Tornado diagram generation - Value at Risk (VaR) and CVaR calculation - Parallel simulation execution ## Used By Processes - Monte Carlo Simulation for Decision Support - Strategic Scenario Development - What-If Analysis Framework - Predictive Analytics Implementation ## Usage ### Distribution Specification ```python # Define input distributions input_variables = { "revenue": { "distribution": "triangular", "parameters": {"min": 800000, "mode": 1000000, "max": 1500000} }, "cost": { "distribution": "normal", "parameters": {"mean": 600000, "std": 50000} }, "market_share": { "distribution": "PERT", "parameters": {"min": 0.05, "mode": 0.10, "max": 0.20} }, "unit_price": { "distribution": "uniform", "parameters": {"m...