Mathematical modeling
Turns engineering, economic, biological and social questions into variables, assumptions, solvable models, validated answers and an honest limits section.
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Types of uncertainty
Random variability and limited knowledge require different uncertainty representations.
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Data provenance
Data records should preserve origin, collection conditions and transformations.
Problem formulation
A modeling question is translated into objectives, variables, assumptions and constraints.
Uncertainty propagation
Input uncertainty induces output uncertainty through a model.
ARIMA and differencing
Difference until stationary, fit autoregressive and moving average terms, and forecast with error growing in the horizon.
Agent-based models
Individual rules and interactions generate collective behavior in simulation.
Knowledge connections
- Data provenance · Related · Map context
- Problem formulation · Related · Map context
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