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In this experiment, the filter parameters are taken to be time invariant, but the noise variance is changed during the simulation, producing a non-stationary signal.
Probabilistic sensitivity analyses were conducted using Monte Carlo simulation, producing incremental cost-effectiveness plots (ICEPs) and cost-effectiveness acceptability curves (CEACs).
The usual way of analyzing those models is via simulation, producing trajectories that provide us with different insights of the system.
Indeed, 42.1% of simulations resulted in a risk of transmission <2% between patients A and B. However, 4.2% of simulations resulted in a risk of transmission >10%, with one simulation producing a risk of 24.0%.
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The simulation produces a predicted or simulated dataset (curves labeled Predicted Data).
simulation produces an idealized trace.
The simulation produces fig 3 from the paper.
In addition, the simulation produced the following probabilities:.
The thermoelectric simulation produced design guidelines for high-performance μTEGs.
A multivariable probabilistic simulation produced similar results to the base-case scenario.
MD simulation produced a less accurate result due to neglecting electronic motions.
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