wehub-resource-sync
36 lines
2.1 KiB
Markdown
36 lines
2.1 KiB
Markdown
---
|
|
allowed-tools: Read, Write, Edit, WebSearch
|
|
argument-hint: [simulation-target] | --financial-projections | --project-timelines | --market-scenarios | --risk-assessment
|
|
description: Run Monte Carlo simulations with probability distributions, confidence intervals, and statistical analysis
|
|
---
|
|
|
|
# Monte Carlo Simulator
|
|
|
|
Run comprehensive Monte Carlo simulations with advanced statistical analysis: **$ARGUMENTS**
|
|
|
|
## Current Analysis Context
|
|
|
|
- Simulation target: Based on $ARGUMENTS (financial projections, project timelines, market scenarios, risk assessment)
|
|
- Key variables: Uncertain parameters that drive outcome variability
|
|
- Available data: Historical data, expert estimates, and probability distributions
|
|
- Decision requirements: Confidence levels and risk tolerance for decision-making
|
|
|
|
## Task
|
|
|
|
Execute sophisticated Monte Carlo simulations with comprehensive uncertainty quantification:
|
|
|
|
**Simulation Target**: Use $ARGUMENTS to simulate financial projections, project timelines, market scenarios, or risk assessments
|
|
|
|
**Monte Carlo Framework**:
|
|
1. **Variable Definition** - Uncertain parameter identification, probability distribution selection, and correlation modeling
|
|
2. **Simulation Engine** - Random sampling, scenario generation, and statistical convergence analysis
|
|
3. **Output Analysis** - Probability distributions, confidence intervals, and sensitivity analysis
|
|
4. **Risk Quantification** - Value at Risk (VaR), extreme scenario analysis, and tail risk assessment
|
|
5. **Scenario Clustering** - Pattern recognition, outcome categorization, and decision-relevant grouping
|
|
6. **Decision Integration** - Risk-adjusted recommendations, optimization strategies, and contingency planning
|
|
|
|
**Advanced Features**: Latin hypercube sampling, copula modeling, importance sampling, and variance reduction techniques.
|
|
|
|
**Statistical Rigor**: Convergence testing, goodness-of-fit validation, and robust statistical inference with comprehensive uncertainty bounds.
|
|
|
|
**Output**: Complete Monte Carlo analysis with probability distributions, risk metrics, scenario analysis, and statistically-grounded decision recommendations with quantified confidence levels. |