Scalable Monte Carlo for Bayesian Learning
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866911958822813696 |
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| author | Fearnhead, Paul Nemeth, Christopher Oates, Chris J. Sherlock, Chris |
| author_facet | Fearnhead, Paul Nemeth, Christopher Oates, Chris J. Sherlock, Chris |
| contents | This book aims to provide a graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC) algorithms, as applied broadly in the Bayesian computational context. Most, if not all of these topics (stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment) have emerged as recently as the last decade, and have driven substantial recent practical and theoretical advances in the field. A particular focus is on methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12751 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scalable Monte Carlo for Bayesian Learning Fearnhead, Paul Nemeth, Christopher Oates, Chris J. Sherlock, Chris Machine Learning Computation Methodology This book aims to provide a graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC) algorithms, as applied broadly in the Bayesian computational context. Most, if not all of these topics (stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment) have emerged as recently as the last decade, and have driven substantial recent practical and theoretical advances in the field. A particular focus is on methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI. |
| title | Scalable Monte Carlo for Bayesian Learning |
| topic | Machine Learning Computation Methodology |
| url | https://arxiv.org/abs/2407.12751 |