`normrnd` — Normally-distributed random numbers with mean mu and standard deviation sigma.

normrnd draws pseudorandom samples from the normal distribution with mean mu and standard deviation sigma. It is part of the Statistics and Machine Learning Toolbox family in MATLAB and Octave's statistics package. RunMat implements it by generating standard normal variates and scaling them as r = mu + sigma * Z where Z ~ N(0, 1).

How `normrnd` works

normrnd(mu, sigma) returns a scalar double drawn from N(mu, sigma^2).
normrnd(mu, sigma, n) returns an n × n matrix of samples.
normrnd(mu, sigma, m, n) returns an m × n matrix of samples.
normrnd(mu, sigma, sz) accepts a size vector and returns an array with shape sz.
mu and sigma must be scalar numeric values.
sigma must be non-negative; normrnd errors if sigma < 0.
All outputs are double precision regardless of input type.

Examples

Single sample from N(0, 1)

rng(0);
r = normrnd(0, 1)

Matrix of Gaussian noise with mean 10 and standard deviation 2

rng(0);
X = normrnd(10, 2, 2, 3)

Simulating normally-distributed measurement errors

rng(0);
trueValue = 100;
sigma = 0.5;
measurements = trueValue + normrnd(0, sigma, 1, 5)

Specifying dimensions with a size vector

rng(0);
T = normrnd(5, 1.5, [2 3])

FAQ

What do mu and sigma mean?

mu is the distribution mean and sigma is the standard deviation. The variance is sigma^2, so normrnd(mu, sigma) draws from N(mu, sigma^2).

What is the workaround if normrnd is unavailable?

normrnd(mu, sigma) is mathematically equivalent to mu + sigma * randn(). For arrays, use mu + sigma * randn(sz) with the same requested size.

Can sigma be zero?

Yes. sigma = 0 is allowed and returns deterministic samples equal to mu. Negative standard deviations are undefined and raise an error.

What use cases does normrnd support?

Common uses include Gaussian noise generation, measurement error simulation, confidence interval experiments, Monte Carlo sampling, and normally-distributed model residuals.

Does normrnd fuse with other operations?

No. Random generation is excluded from fusion planning to preserve statistical properties.

How do I control reproducibility?

Use rng before calling normrnd to seed the global generator.

Open-source implementation

Unlike proprietary runtimes, every RunMat function is open-source. Read exactly how normrnd works, line by line, in Rust.

View normrnd.rs on GitHub
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Found a bug? Open an issue with a minimal reproduction.

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normrnd — Normally-distributed random numbers with mean mu and standard deviation sigma.

How normrnd works