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Scalable Monte Carlo for Bayesian Learning

arXiv 2407.12751 ↗First posted 17 Jul 2024stat.ML cs.LG stat.COcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 168 references that are in the map. First posted on arXiv 17 Jul 2024.

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20 of 168 references.

References · 20

  1. 2014Stochastic Gradient Hamiltonian Monte Carlocited by 341
  2. 2015A Complete Recipe for Stochastic Gradient MCMCcited by 146
  3. 2017Measuring Sample Quality with Kernelscited by 119
  4. 2018The promises and pitfalls of Stochastic Gradient Langevin Dynamicscited by 56
  5. 2017Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlocited by 43
  6. 2024BlackJAX: Composable Bayesian inference in JAXcited by 24
  7. 2020Sliced Kernelized Stein Discrepancycited by 23
  8. 2022Gradient Estimation with Discrete Stein Operatorscited by 22
  9. 2018On the Theory of Variance Reduction for Stochastic Gradient Monte Carlocited by 19
  10. 2022Targeted Separation and Convergence with Kernel Discrepanciescited by 15
  11. 2020Optimal quantisation of probability measures using maximum mean discrepancycited by 13
  12. 2022Controlling Moments with Kernel Stein Discrepanciescited by 13
8 more
  1. 2017The True Cost of Stochastic Gradient Langevin Dynamicscited by 11
  2. 2020The reproducing Stein kernel approach for post-hoc corrected samplingcited by 10
  3. 2017Stochastic Gradient MCMC Methods for Hidden Markov Modelscited by 8
  4. 2016Quantifying the accuracy of approximate diffusions and Markov chainscited by 7
  5. 2017Piecewise-Deterministic Markov Chain Monte Carlocited by 6
  6. 2021PDMP Monte Carlo methods for piecewise-smooth densitiescited by 5
  7. 2021The Apogee to Apogee Path Samplercited by 5
  8. 2020NuZZ: numerical Zig-Zag sampling for general modelscited by 2

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