Sampling from only feasible points
Type: Model example, Extra
Sampling from only feasible points: You have a bunch of chance variables, each with a probability distribution. Their joint sample, however, contains some combinations of points that are (for one reason or another) physically impossible. We'll call those infeasible points. You'd like to eliminate those points from the sample and keep only the feasible points.
This module implements a button that will sample a collection of chance variables, then reset the sample size and keep only those sample points that are "feasible".
Obviously, this approach will work best when most of your samples are feasible. If you can handle the "infeasible" points in your model directly, by conditioning certain chance variables on others, that is far preferable. But there are some cases where this solution (although a bit of a kludge) is more convenient.
The instructions for how to use this are in the module description field.
Download: Feasible Sampler.ana
Author: Lonnie Chrisman
Keywords: Statistics, Uncertainty, User interface, sampling, Importance sampling, feasibility, Monte Carlo simulation