Simulating random variates from the Pearson IV and betaized Meixner-Morris distributions
Luc Devroye, Joe R. Hill
TL;DR
This paper introduces efficient algorithms for generating random variates from the Pearson IV and betaized Meixner-Morris distributions, enabling accurate sampling across their entire parameter ranges.
Contribution
It presents the first uniformly fast generators for Pearson IV and an efficient method for the betaized Meixner-Morris distribution.
Findings
Fast, accurate sampling algorithms developed
Applicable over entire parameter ranges
Enhanced computational efficiency for these distributions
Abstract
We develop uniformly fast random variate generators for the Pearson IV distribution that can be used over the entire range of both shape parameters. Additionally, we derive an efficient algorithm for sampling from the betaized Meixner-Morris density, which is proportional to the product of two generalized hyperbolic secant densities.
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Taxonomy
TopicsStatistical Distribution Estimation and Applications · Bayesian Methods and Mixture Models · Statistical Methods and Bayesian Inference