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Discrete Distributions

This guide summarizes each discrete distribution currently provided in applpy.distributions.discrete.

BenfordRV

Description: Benford's law distribution for leading digits. Parameters: none. Typical uses: fraud/anomaly detection and first-digit frequency checks.

from applpy.conversion import pdf
from applpy.distributions.discrete import BenfordRV

x = BenfordRV()
print(pdf(x, 1))

BernoulliRV

Description: Bernoulli distribution for single yes/no trials. Parameters: p (success probability in (0, 1)). Typical uses: binary outcomes, click/no-click events, and pass/fail indicators.

from applpy.moments import mean
from applpy.distributions.discrete import BernoulliRV

x = BernoulliRV(p=0.3)
print(mean(x))

BinomialRV

Description: Binomial distribution for number of successes across fixed independent trials. Parameters: N (number of trials, positive integer), p (success probability in (0, 1)). Typical uses: conversion counts, defect counts, and repeated binary experiments.

from applpy.conversion import cdf
from applpy.distributions.discrete import BinomialRV

x = BinomialRV(N=10, p=0.4)
print(cdf(x, 5))

GeometricRV

Description: Geometric distribution for number of trials until first success. Parameters: p (success probability in (0, 1)). Typical uses: waiting-time counts for repeated Bernoulli processes.

from applpy.moments import mean
from applpy.distributions.discrete import GeometricRV

x = GeometricRV(p=0.25)
print(mean(x))

PoissonRV

Description: Poisson distribution for event counts in a fixed interval. Parameters: theta (rate/intensity, positive). Typical uses: arrivals, incident counts, and rare-event modeling.

from applpy.conversion import cdf
from applpy.distributions.discrete import PoissonRV

x = PoissonRV(theta=3)
print(cdf(x, 2))

UniformDiscreteRV

Description: Discrete uniform distribution over equally likely integer values in a range. Parameters: a (lower bound), b (upper bound), k (step size, default 1). Typical uses: fair integer sampling and simple finite-state simulations.

from applpy.conversion import pdf
from applpy.distributions.discrete import UniformDiscreteRV

x = UniformDiscreteRV(a=1, b=6)
print(pdf(x, 3))