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Efficient and generalizable tuning strategies for stochastic gradient MCMC

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Bibliographic Details
Main Authors: Jeremie, Coullon, Leah, South, Christopher, Nemeth
Format: Book
Language:English
Published: Springer 2023
Subjects:
SGMCMC
requiring alternative tools and diagnostics
Online Access:https://link.springer.com/article/10.1007/s11222-023-10233-3
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7700
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spelling oai:localhost:PNK-77002023-04-10T01:57:28Z Efficient and generalizable tuning strategies for stochastic gradient MCMC Jeremie, Coullon Leah, South Christopher, Nemeth SGMCMC requiring alternative tools and diagnostics CC BY Stochastic gradient Markov chain Monte Carlo (SGMCMC) is a popular class of algorithms for scalable Bayesian inference. However, these algorithms include hyperparameters such as step size or batch size that influence the accuracy of estimators based on the obtained posterior samples. As a result, these hyperparameters must be tuned by the practitioner and currently no principled and automated way to tune them exists. Standard Markov chain Monte Carlo tuning methods based on acceptance rates cannot be used for SGMCMC, thus requiring alternative tools and diagnostics. 2023-04-10T01:57:28Z 2023-04-10T01:57:28Z 2023 Book https://link.springer.com/article/10.1007/s11222-023-10233-3 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7700 en application/pdf Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic SGMCMC
requiring alternative tools and diagnostics
spellingShingle SGMCMC
requiring alternative tools and diagnostics
Jeremie, Coullon
Leah, South
Christopher, Nemeth
Efficient and generalizable tuning strategies for stochastic gradient MCMC
description CC BY
format Book
author Jeremie, Coullon
Leah, South
Christopher, Nemeth
author_facet Jeremie, Coullon
Leah, South
Christopher, Nemeth
author_sort Jeremie, Coullon
title Efficient and generalizable tuning strategies for stochastic gradient MCMC
title_short Efficient and generalizable tuning strategies for stochastic gradient MCMC
title_full Efficient and generalizable tuning strategies for stochastic gradient MCMC
title_fullStr Efficient and generalizable tuning strategies for stochastic gradient MCMC
title_full_unstemmed Efficient and generalizable tuning strategies for stochastic gradient MCMC
title_sort efficient and generalizable tuning strategies for stochastic gradient mcmc
publisher Springer
publishDate 2023
url https://link.springer.com/article/10.1007/s11222-023-10233-3
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7700
_version_ 1762818480037429248
score 8.887836

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