import numpy as np import matplotlib.pyplot as plt def nadar_log_pmf(k, theta): """Compute PMF for Nadar Log distribution.""" norm = -np.log(1 - theta) return (theta**k) / (k * norm)
First, compute the normalizer: ( -\ln(1-0.8) = -\ln(0.2) = 1.60944 )
[ P(X = k) = \frac\theta^k-k \ln(1-\theta), \quad k = 1, 2, 3, \dots ] nadar log pdf
This write-up explores the mathematical foundation, key properties, applications, and generation of the Probability Density Function (PDF) for the Nadar Log distribution. The Nadar Log distribution is a discrete distribution (support ( k = 1, 2, 3, \dots )) whose probability mass function is proportional to a logarithmic series. The standard form of its PDF (or more accurately, its Probability Mass Function, since it's discrete) is given by:
Understanding this distribution equips data scientists and statisticians with another lens through which to view and model real-world count data. import numpy as np import matplotlib
theta = 0.7 k_values = np.arange(1, 21) pmf_values = nadar_log_pmf(k_values, theta)
[ -\ln(1-\theta) = \theta + \frac\theta^22 + \frac\theta^33 + \dots = \sum_k=1^\infty \frac\theta^kk ] theta = 0
plt.stem(k_values, pmf_values) plt.title(f'Nadar Log PDF (θ = theta)') plt.xlabel('k') plt.ylabel('P(X=k)') plt.grid(alpha=0.3) plt.show() The Nadar Log PDF (Logarithmic distribution) is a discrete, heavy-tailed probability model derived directly from the logarithmic series. Its key characteristics—mode at 1, overdispersion, and polynomial tail decay—make it a powerful tool for modeling rare event counts in ecology, linguistics, and beyond. While less common than the normal or Poisson distributions, it occupies a critical niche for data where small values dominate but large values occur more frequently than exponential models would predict.