{"title":"A General Purpose Approximation to the Ferguson-Klass Algorithm for Sampling from Lévy Processes Without Gaussian Components","authors":"Dawid Bernaciak, Jim E. Griffin","doi":"arxiv-2407.01483","DOIUrl":null,"url":null,"abstract":"We propose a general-purpose approximation to the Ferguson-Klass algorithm\nfor generating samples from L\\'evy processes without Gaussian components. We\nshow that the proposed method is more than 1000 times faster than the standard\nFerguson-Klass algorithm without a significant loss of precision. This method\ncan open an avenue for computationally efficient and scalable Bayesian\nnonparametric models which go beyond conjugacy assumptions, as demonstrated in\nthe examples section.","PeriodicalId":501215,"journal":{"name":"arXiv - STAT - Computation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - STAT - Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.01483","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We propose a general-purpose approximation to the Ferguson-Klass algorithm
for generating samples from L\'evy processes without Gaussian components. We
show that the proposed method is more than 1000 times faster than the standard
Ferguson-Klass algorithm without a significant loss of precision. This method
can open an avenue for computationally efficient and scalable Bayesian
nonparametric models which go beyond conjugacy assumptions, as demonstrated in
the examples section.
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