Scalable Monte Carlo for Bayesian Learning
Scalable Monte Carlo for Bayesian Learning
Christopher Nemeth, Lancaster University
Chris J. Oates, University of Newcastle upon Tyne
Chris Sherlock, Lancaster University
$64.99
USDA 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.
- Focuses on scalable Bayesian inference techniques for large datasets, making it highly relevant for modern data science and machine learning challenges
- Covers cutting-edge MCMC techniques like non-reversible MCMC and piecewise deterministic Markov processes (PDMPs)
- Provides practical examples to demonstrate how scalable Bayesian learning methods can be implemented
Product details
July 2025Hardback
9781009288446
247 pages
229 × 152 mm
Not yet published - available from July 2025
Table of Contents
- Preface
- 1. Background
- 2. Reversible MCMC and its Scaling
- 3. Stochastic Gradient MCMC Algorithms
- 4. Non-Reversible MCMC
- 5. Continuous-Time MCMC
- 6. Assessing and Improving MCMC
- References
- Index.
