Examples¶

Runnable scripts live in examples/.Docs snippets below mirror them.

Gallery¶

Normal sigma morph CLT convergence Copula dependence Beta morph Mixture separation

All GIFs are generated reproducibly via python examples/generate_media.py.

Basic usage (`examples/basic_usage.py`)¶

python examples/basic_usage.py

Covers Normal/Exponential/Binomial PDF+CDF plots plus a Normal comparison chart.

CLT demonstration¶

from probviz.distributions import UniformDistribution, NormalDistribution
import numpy as np

uniform = UniformDistribution(a=0, b=1)
means = [np.mean(uniform.rvs(size=n, random_state=i)) for i in range(2000) for n in [30]]

(See README.md for the full 2×2 subplot version.)

Binomial vs Poisson approximation¶

from probviz.distributions import BinomialDistribution, PoissonDistribution
import numpy as np

n, p = 100, 0.03
b = BinomialDistribution(n=n, p=p)
approx = PoissonDistribution(lambda_param=n * p)
x = np.arange(0, 15)
print(b.pdf(x) - approx.pdf(x))  # small residuals validate the approximation

Fitting + Monte Carlo¶

See Fitting and Monte Carlo for copy-pasteable DistributionFitter and MonteCarloSimulator snippets.