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Simulations

Simulate values, outcomes, and run what-if experiments.

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Purpose

Enables comprehensive Monte Carlo simulations and probabilistic modeling to quantify uncertainty, evaluate risks, and simulate complex real-world scenarios across diverse domains.

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Key functions

  • CreateMetalogDistribution — Fits a flexible "any-shape" distribution to data, capturing skewness and multimodality.

  • CreateMetalogDistributionFromPercentiles — Reconstructs a full probability distribution from summary statistics (e.g., P10, P50, P90).

  • CreateSIPDataframe — Generates a "Stochastic Information Packet" (SIP) structure, creating localized trial dataframes for lists of items to standardize Monte Carlo inputs.

  • CreateCorrelatedSIPs — Generates pairs of random variables with a precise Pearson correlation to preserve dependencies.

  • CreateSLURPDistributionFromLinearRegression — Creates SLURP distributions from linear regression models to simulate outcomes with uncertainty.

  • CreateSLURPDistributionFromLogisticRegression — Simulates probability distributions from logistic regression models, capturing classification uncertainty.

  • CreateSLURPDistributionFromExponentialSmoothing — Generates uncertainty distributions for future time-series forecasts using exponential smoothing models.

  • SimulateNormallyDistributedOutcome — Conducts standard Monte Carlo sampling for variables following a normal (Gaussian) distribution.

  • SimulateTDistributedOutcome — Simulates outcomes using a Student's T distribution, useful for small sample sizes or "fat-tailed" risks.

  • SimulateCountOutcome — Simulates the count of discrete events occurring in a fixed interval (Poisson distribution).

  • SimulateCountOfSuccesses — Simulates the number of successes in a fixed number of trials (Binomial distribution).

  • SimulateCountUntilFirstSuccess — Simulates the number of trials required to achieve the first success (Geometric Wait-Time).

  • SimulateTimeBetweenEvents — Simulates the duration between independent events occurring at a constant rate (Exponential distribution).

  • SimulateTimeUntilNEvents — Simulates the cumulative time required for a specific number of events to occur (Gamma distribution).

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Common use cases

  • Risk Analysis: Quantifying the probability of extreme events, such as financial market crashes, environmental disasters, or supply chain disruptions.

  • Healthcare & Epidemiology: Modeling disease incubation periods, recovery times, and patient flow through hospital systems under varying conditions.

  • Intelligence & Security: Simulating signal latency, target movement patterns, and detection probabilities across multiple operational phases.

  • Project & Financial Management: Estimating task durations, project schedule risks, and Value at Risk (VaR) for interdependent asset portfolios.

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