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  3. Scalable Monte Carlo for Bayesian Learning
  • Cited by 1
Publisher:
Cambridge University Press
Online publication date:
May 2025
Print publication year:
2025
Online ISBN:
9781009288460
DOI:
https://doi.org/10.1017/9781009288460
Series:
Institute of Mathematical Statistics Monographs
Subjects:
Computer Science, Pattern Recognition and Machine Learning, General Statistics and Probability, Applied Probability and Stochastic Networks, Statistics and Probability
Information

Book description

A graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC), as applied broadly in the Bayesian computational context. The topics covered have emerged as recently as the last decade and include stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment. A particular focus is on cutting-edge 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. Examples are woven throughout the text to demonstrate how scalable Bayesian learning methods can be implemented. This text could form the basis for a course and is sure to be an invaluable resource for researchers in the field.

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Contents

Contents
  • 1 - Background
    pp 1-38
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