Abstract
The Markov Chain Monte Carlo (MCMC) methods based on the Bayes theorem are used when an a posteriori distribution does not have a tractable form and is therefore not fully known or directly usable (e.g., for maximum a posteriori parameter estimation). MCMC methods overcome intractability by drawing parameter values from known distributions and correlating these drawings until they approximately match the target distribution. MCMC methods represent a powerful class of algorithms for processing data and knowledge, which is why they are also called a "quantum leap in statistics".
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Neifer, T. (2024). Markov Chain Monte Carlo Methods. In: Peren, F.W., Neifer, T. (eds) Operations Research and Management. Springer Texts in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-47206-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-47206-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47205-3
Online ISBN: 978-3-031-47206-0
eBook Packages: Business and ManagementBusiness and Management (R0)