A tool for random number generation on a distribution, such as a triangular distribution or PERT distribution, is very convenient for making estimates for physical values in the real world. Scientists and engineers often make estimates or assumptions, but they are also able to bracket the estimate based on experience or physics. For example, in estimating the mass of a small object, a normal distribution centered at my best guess could result in negative mass, which is known to be impossible. A triangular distribution allows quick and simple characterization of values as "probably M, but definitely not less than A or more than B."
The formulations here have some features that make it easy to integrate this capturing of uncertainty into scripts.
Example: Based purely on guesstimates that include a best guess and intuitive upper/lower limits, how much do a dime, nickel, and quarter weigh together?
% First, create a function based on randt to conveniently define and generate "uncertain variables."
uvar = @(x) randt(x,[1e5,1]);
dime = uvar([1 1.5 3.5]); %Pretty light, but not less than a gram..."
nickel = uvar([3 5 6]);
quarter = uvar([5 8 10]);
total = dime + nickel + quarter;
Sky Sartorius (2021). Pseudorandom number generation for engineering estimates (https://github.com/sky-s/randx), GitHub. Retrieved .
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