Obviously, calculations that use the same "random" numbers cannot be thought of as statistically independent. But as mentioned above, each time you restart MATLAB those functions are reset and return the same sequences of numbers. Non-RepeatabilityĮach time you call rand, randi, or randn, they draw a new value from their shared random number generator, and successive values can be treated as statistically independent. For now, it serves as a way to see what generator rand, randi, and randn are currently using. You'll see in more detail below how to use the above output, including the State field, to control and change how MATLAB generates random numbers. Rng("default") provides a very simple way to put the random number generator back to its default settings. For more information, see Default Settings for Random Number Generator and Reproducibility for Random Number Generator. If you do not change these preferences, then rng uses the factory value of "twister" for the Mersenne Twister generator with seed 0, as in previous releases. Starting in R2023b, you can set the default algorithm and seed in MATLAB preferences. When you first start a MATLAB session or call rng("default"), MATLAB initializes the random number generator using the default algorithm and seed. For example, you might want to repeat a calculation that involves random numbers, and get the same result. It's often useful to be able to reset the random number generator to that startup state, without actually restarting MATLAB. If you look at the output from rand, randi, or randn in a new MATLAB session, you'll notice that they return the same sequences of numbers each time you restart MATLAB.
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