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Fitting & Model Selection (src.fitting)

  • DistributionFitter(data) — fit_all() / fit_distribution(name) across Normal, Exponential, Gamma (MLE + method-of-moments), Beta, Weibull, Lognormal; plus qq_plot_data and calculate_residuals.
  • BayesianEstimator(data) — conjugate posteriors for Normal mean/variance, Poisson rate, Bernoulli p.
  • GoodnessOfFit — static chi-square, Kolmogorov–Smirnov, Anderson–Darling, Shapiro–Wilk, and Jarque–Bera tests returning plain dicts.
from probviz.fitting import DistributionFitter
import numpy as np

rng = np.random.default_rng(0)
data = rng.normal(0, 1, size=500)
fitter = DistributionFitter(data)
print(fitter.fit_all())   # ranked by goodness-of-fit