Definition:
A computational technique that uses random sampling to obtain numerical results, often used to assess the impact of risk and uncertainty in predictive models.
Key Components:
- Random Sampling: Generating random inputs based on defined probability distributions.
- Iterative Process: Running numerous simulations to explore a wide range of outcomes.
- Statistical Analysis: Aggregating results to determine probabilities of different scenarios.
Use Cases/Industries:
- Project Management: Estimating project completion times and budget requirements.
- Finance: Risk assessment in investment portfolios.
- Energy Sector: Forecasting commodity price fluctuations.
Advantages:
- Comprehensive Risk Analysis: Provides a clearer picture of uncertainty.
- Improved Decision-Making: Helps in resource allocation and contingency planning.
- Flexibility: Can be applied across industries.
Challenges:
- Computationally Intensive: Requires significant processing power for large datasets.
- Data Sensitivity: Results are highly dependent on input assumptions.
- Complexity: Interpretation of results can be challenging for non-experts.
Related Terms:
Probabilistic Modeling, Risk Analysis, Sensitivity Analysis
Example:
A project manager uses Monte Carlo simulation to predict the probability of meeting a project deadline, considering various risk factors.
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Synonyms:
Stochastic Simulation, Random Sampling Method, Probability Simulation
