An efficient algorithm for sampling from \sin^k(x) for generating\n random correlation matrices

2018/09/13 by Enes Makalic, Daniel F. Schmidt, Makalic, Enes +1
Computer Science · Mathematics · #Advanced Combinatorial Mathematics #Bayesian Methods and Mixture Models #Cellular Automata and Applications #Computation (stat.CO) #FOS: Computer and information sciences

paper · pdf · doi:10.48550/arxiv.1809.05212

openalex publication_date 2018/09/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/11

Abstract

In this note, we develop a novel algorithm for generating random numbers from\na distribution with a probability density function proportional to \sin^k(x),\nx \∈ (0,\π) and k \≥ 1. Our algorithm is highly efficient and is based\non rejection sampling where the envelope distribution is an appropriately\nchosen beta distribution. An example application illustrating how the new\nalgorithm can be used to generate random correlation matrices is discussed.\n

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