A lightweight bounded random number generator for everyday needs, visualised with a number line and histogram.
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Distribution
Probability Distributions
Random numbers are generated using a pseudorandom number generator drawn from the following probability distributions:
Uniform Distribution
A uniform distribution means every value in a range is equally likely. A simple example is the angle of a perfectly balanced spinner such that any direction has the same chance of appearing. There are no preferred values and no clustering, just flat, even randomness.
Gaussian (Normal) Distribution
The Gaussian distribution shows up when many small random effects combine. A good example is thermal noise in electronic circuits caused by the random motion of electrons. Most values stay near the average, while large deviations are rare, forming a bell curve.
Laplacian Distribution
The Laplacian distribution models situations with lots of small changes and occasional big jumps. An example is pixel differences in images where neighbouring pixels are similar, but edges create sharp changes. This produces a sharp peak with heavier tails than a Gaussian.
Exponential Distribution
The exponential distribution describes waiting times between random events. Radioactive decay is a textbook example where each atom has a constant chance of decaying at any moment. Short waits are common, long waits are possible but unlikely.
Triangular Distribution
The triangular distribution is useful when you know the minimum, maximum, and most likely value. For example, estimating how long a task might take when you only have rough bounds. Values near the middle are more likely, while extremes are less common.