Talos Guides
Monte Carlo
Run probabilistic ranges for uncertain assumptions and downside probability.
Purpose
Monte Carlo helps explain distribution, not just a single point estimate.
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When to use it
Several assumptions are uncertain.
Downside risk matters to the decision.
You need a richer risk story for Pro or Pro+ reports.
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Inputs needed
Variables
Ranges
Correlations when known
Iteration count
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How to read the output
Focus on probability of loss and percentile outcomes.
High iterations are Pro+ when available.
Results are only as good as the input distributions.
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Common mistakes
Using random ranges without rationale.
Treating the median as certainty.
Ignoring correlation between assumptions.
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Next steps
Sensitivity
Decision notes
Investor memo
Professional note
Probabilistic output should be paired with plain-language limitations.