[Edit: I include a specific example for clarity]
Say I have two models:
Model 1) fuel_consumption_model, which calculates the fuel consumption of barges. It has about a dozen input parameters that all have distributions. It takes about 10 minutes to run 1000 times.
Model 2) emissions_model, which calculates the emissions of barges based on several independent parameters, one of which is fuel consumption
I want to run Model 2 1000 times to get the distribution of emissions (the model output). I could link up the two models, but that would add a lot of computing time because I would be recalculating the fuel_consumption_model outputs.
So instead I:
(1) run Model 1 N times, and store the results (fuel consumptions) in a Nx1 array;
(2) reuse the fuel consumption array directly in Model 2. Specifically, I randomly take a value from the Model 1 output array for each Monte Carlo Simulation iteration.
I'd like to read up on the use of this approach in other fields, and also read the opinion of the statistical community on this type of approach. Unfortunately, my search has not yielded anything.
I've read this about preemptively sampling a known distribution, and I understand the criticism, but in my case, it would be unthinkable to generate samples for my input parameters every time the large model is run, nor to describe parametrically the distribution of these input parameters.
I've also read on non-parametric models, but am not sure it entirely applies to what I am doing.
I'd appreciate any pointers, key words or opinions.