Skip to main content Accessibility help
×
  1. Home
  2. Books
  3. Scalable Monte Carlo for Bayesian Learning
  • Cited by 1
      • Show more authors
      • You may already have access via personal or institutional login
    • Select format
    • Publisher:
      Cambridge University Press
      Publication date:
      May 2025
      June 2025
      ISBN:
      9781009288460
      9781009288446
      DOI:
      https://doi.org/10.1017/9781009288460
      Dimensions:
      (229 x 152 mm)
      Weight & Pages:
      0.51kg, 250 Pages
      Dimensions:
      Weight & Pages:
      • 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
    You may already have access via personal or institutional login
    • Selected: Digital
    Add to cart View cart Buy from Cambridge.org
    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.

    Refine List

    Actions for selected content:

    Select all | Deselect all
    • View selected items
    • Export citations
    • Download PDF (zip)
    • Save to Kindle
    • Save to Dropbox
    • Save to Google Drive

    Save Search

    You can save your searches here and later view and run them again in "My saved searches".

    Please provide a title, maximum of 40 characters.
    Cancel
    ×

    Contents

    Contents
    • 1 - Background
      pp 1-38
        • You have access
      • PDF
      • Export citation
    References

    Metrics

    Full text views

    Total number of HTML views: 0
    Total number of PDF views: 0 *
    Loading metrics...

    Book summary page views

    Total views: 0 *
    Loading metrics...

    * Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

    Usage data cannot currently be displayed.

    Accessibility standard: Unknown

    Why this information is here

    This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.

    Accessibility Information

    Accessibility compliance for the PDF of this book is currently unknown and may be updated in the future.