The Ziggurat Algorithm is an efficient method for generating pseudo-random numbers from specific probability distributions, notably the normal (Gaussian) distribution. Developed by George Marsaglia and others in the 1960s, it belongs to the class of rejection sampling algorithms and relies on precomputed tables to expedite the sampling process.
How It Works:
The algorithm partitions the desired probability distribution into horizontal layers, each with a uniform probability density. By randomly selecting points within these layers and applying a rejection criterion, the algorithm efficiently samples from the target distribution. This approach significantly reduces computational complexity compared to methods like the Marsaglia polar method or the Box–Muller transform, which require more intensive mathematical operations such as logarithms and square roots.
« Back to Glossary Index