deterministicGauss1​D() – Deterministic Gaussian Samples

Optimally placed samples of the standard normal density in the scalar case


Updated 12 Dec 2020

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samples = deterministicGauss1D( L )

Deterministic Sampling of Standard Normal Gaussian Distribution

Advantages of deterministic sampling vs random sampling like randn()
- Reproducible results
- Samples are optimally placed
- Less samples needed for same quality results
- Methods may fail occassionally due to poor choice of random samples

- L : number of samples

- samples : (L x 1) vector with 1D sample locations

Example : Get 7 deterministic samples with a standard deviation of 3 and a mean of 5.
>> samples = deterministicGauss1D(5)*3 + 5; : Theory of deterministic sampling in general : Deterministic Gaussian sampling in higher dimensions : Deterministic sampling in non-Euclidean manifolds

Cite As

Daniel Frisch (2023). deterministicGauss1D() – Deterministic Gaussian Samples (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2020b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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