Using simulation to handle implicit likelihoods in a Bayesian analysis

Autor:	David Scott, Todd L. Graves, Aparna V. Huzurbazar, Brian J. Williams, Crystal D. Linkletter, C. S. Reese, Richard L. Warr, Nicolas W. Hengartner, Dave Higdon, Michael S. Hamada, Earl Lawrence, Randy R. Sitter
Rok vydání:	2021
Předmět:	021103 operations research Computer science business.industry Bayesian probability 0211 other engineering and technologies 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Industrial and Manufacturing Engineering Statistics::Computation 010104 statistics & probability ComputingMethodologies_PATTERNRECOGNITION Data model Statistics::Methodology Artificial intelligence 0101 mathematics Safety Risk Reliability and Quality business computer Computer Science::Databases
Zdroj:	Quality Engineering. 33:172-177
ISSN:	1532-4222 0898-2112
DOI:	10.1080/08982112.2020.1814959
Popis:	This article presents a Bayesian inferential method where the likelihood for a model is unknown, i.e., an implicit likelihood, but where data can easily be simulated from the data model. We use sim...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::71c8e687e598ed061cf3f4c981397bbb https://doi.org/10.1080/08982112.2020.1814959 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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