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Monte Carlo (src.monte_carlo)

  • MonteCarloSimulator(random_seed) — simulate, estimate_probability, estimate_expectation, importance_sampling, stratified_sampling, bootstrap, permutation_test, with convergence tracking in SimulationResult.
  • VarianceReduction — antithetic variates, control variates.
  • QuasiMonteCarloSimulator — Halton/Sobol sequences and QMC integration.
from probviz.monte_carlo import MonteCarloSimulator
import numpy as np

rng = np.random.default_rng(42)
sim = MonteCarloSimulator(random_seed=42)
res = sim.estimate_probability(lambda: rng.normal(0, 1) > 1.0, num_samples=100_000)
print(res["probability"], res["confidence_interval"])