Review:
Monte Carlo Simulation
overall review score: 4.5
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score is between 0 and 5
Monte Carlo simulation is a computational technique used to model the probability of different outcomes in a process that cannot easily be predicted due to the intervention of random variables.
Key Features
- Uses random sampling to model uncertainty
- Can be used in various fields, including finance, physics, and engineering
- Provides a range of possible outcomes instead of a single prediction
Pros
- Great for analyzing complex systems with many variables
- Allows for the incorporation of uncertainty in predictions
- Provides a visual representation of possible outcomes
Cons
- Requires a large number of iterations to produce accurate results
- Can be time-consuming and computationally intensive
- Assumes that all variables are independent, which may not always be the case