A Scalable Bayesian Sampling Method Based on Stochastic Gradient Descent Isotropization

Stochastic gradient sg-based algorithms for Markov chain Monte Carlo sampling (sgmcmc) tackle large-scale Bayesian modeling problems by operating on mini-batches and injecting noise on sgsteps. The sampling properties of these algorithms are determined by user choices, such as the covariance of the...

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Detalles Bibliográficos
Autores principales: Franzese, Giulio, Milios, Dimitrios, Filippone, Maurizio, Michiardi, Pietro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8626050/
https://www.ncbi.nlm.nih.gov/pubmed/34828123
http://dx.doi.org/10.3390/e23111426

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8626050/
https://www.ncbi.nlm.nih.gov/pubmed/34828123
http://dx.doi.org/10.3390/e23111426

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