Match each of the modeling analysis techniques with how it is used.

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Analyzes an equally spaced, sequenced set of data for relationships based on different lags in the sequenced data.

Uses a prior distribution or prior model along with a set of observations to obtain a new posterior distribution or posterior model. Often used for physical systems with systematic underlying models and decision analysis.

Models outcomes based on repeated random sampling. Often used for optimization, risk analysis, and physical systems with quantum interactions.

Uses a neural net structure to create a complex model based on simple node inputs and outputs. Often used for pattern recognition in large data sets.

A. Monte Carlo

B. machine learning

C. time-series

D. Bayesian