Scalable Monte Carlo for Bayesian Learning
Book Details
Format
Hardback or Cased Book
Book Series
Institute of Mathematical Statistics Monographs
ISBN-10
100928844X
ISBN-13
9781009288446
Publisher
Cambridge University Press
Imprint
Cambridge University Press
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Jun 5th, 2025
Print length
247 Pages
Weight
504 grams
Dimensions
16.10 x 23.70 x 2.10 cms
Product Classification:
Probability & statistics
Ksh 9,050.00
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An intuitive introduction to advanced topics in Markov chain Monte Carlo (MCMC), presenting cutting-edge developments that address the crucial issue of scalability. It could form the basis for a graduate-level course and will be a valuable resource for researchers in the field.
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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